abink/Tumor_Classification

1{"cells":[{"cell_type":"markdown","source":["# **Brain MRI Tumor Classification Using EfficientNetB0 Transfer Learning**"],"metadata":{"id":"kFRakAU-uEOm"}},{"cell_type":"markdown","source":["# **Architecture**\n","EfficientNetB0 (ImageNet pretrained) → GlobalAveragePooling2D → Dense(256) + BatchNorm + Dropout(0.4) → Dense(128) + BatchNorm + Dropout(0.3) → Dense(4, Softmax)\n","\n","# **Results**\n","\n","Initial accuracy: **33%**\n","\n","Fine-tuned accuracy: **35%**\n","\n","The model showed severe class-collapse behavior, including failure to identify Glioma and No Tumor reliably.\n","Therefore, EfficientNetB0 was not selected for further development."],"metadata":{"id":"rs5cGtfcuYvj"}},{"cell_type":"code","source":[],"metadata":{"id":"y03kSA4AIt__"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qjQrXUwrIZdN"},"source":["**Libraries and datasets**"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":26,"status":"ok","timestamp":1787458188355,"user":{"displayName":"Abin K","userId":"04862970659921921970"},"user_tz":-330},"id":"dVQpaeHGI1eK"},"outputs":[],"source":["import tensorflow as tf\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Input, Flatten, Dropout, Dense, GlobalAveragePooling2D, BatchNormalization\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.applications import EfficientNetB0"]},{"cell_type":"code","source":["import os\n","import random"],"metadata":{"id":"cRXa2A8v7Lmy","executionInfo":{"status":"ok","timestamp":1787458022543,"user_tz":-330,"elapsed":4,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["from PIL import Image, ImageEnhance\n","from tensorflow.keras.preprocessing.image import load_img\n","from sklearn.utils import shuffle"],"metadata":{"id":"ziTpmjh_7I25","executionInfo":{"status":"ok","timestamp":1787458022545,"user_tz":-330,"elapsed":4,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"qsuWozrTMP4R","executionInfo":{"status":"ok","timestamp":1787458022548,"user_tz":-330,"elapsed":2,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n","from sklearn.preprocessing import label_binarize\n","from tensorflow.keras.models import load_model"],"metadata":{"id":"nxAAPGa757Ex","executionInfo":{"status":"ok","timestamp":1787458023939,"user_tz":-330,"elapsed":1389,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"yqVnKH849X3f","executionInfo":{"status":"ok","timestamp":1787458023966,"user_tz":-330,"elapsed":3,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["print(\"GPU:\", tf.config.list_physical_devices('GPU'))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t2yKNLVc55Z4","executionInfo":{"status":"ok","timestamp":1787458025568,"user_tz":-330,"elapsed":9,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"301ed35d-0451-4708-cd60-fc984efbe88f"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"]}]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J3yE0IytKG1c","executionInfo":{"status":"ok","timestamp":1787458043705,"user_tz":-330,"elapsed":18135,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"a0546e9f-0553-4f09-9ab2-d87e4e34a34e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["train_dir = '/content/drive/MyDrive/cancer-cnn/extracted data/Training'\n","test_dir = '/content/drive/MyDrive/cancer-cnn/extracted data/Testing'"],"metadata":{"id":"ZoFscuOz6Jv8","executionInfo":{"status":"ok","timestamp":1787458091922,"user_tz":-330,"elapsed":5,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"UrsAzHg66PfS","executionInfo":{"status":"ok","timestamp":1787458093471,"user_tz":-330,"elapsed":24,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"r2cZxSxzMgSd"},"source":["**preprocessing img**"]},{"cell_type":"code","source":["#img Augmentation fun\n","def augment_image(image):\n","    image = Image.fromarray(np.uint8(image))\n","    image = ImageEnhance.Brightness(image).enhance(random.uniform(0.8, 1.2))#brightness -random\n","    image = ImageEnhance.Contrast(image).enhance(random.uniform(0.8, 1.2))  #contrast -random\n","    image = np.array(image) / 255.0                                         #normalize pixel values to [0, 1]\n","    return image\n","\n","#load img & apply augmentation\n","def open_images(paths):\n","    images = []\n","    for path in paths:\n","        image = load_img(path, target_size=(IMAGE_SIZE, IMAGE_SIZE))\n","        image = augment_image(image)\n","        images.append(image)\n","    return np.array(images)\n","\n","#encoding labels (label names)\n","def encode_label(labels):\n","    unique_labels = os.listdir(train_dir)  # Ensure unique labels are determined\n","    encoded = [unique_labels.index(label) for label in labels]\n","    return np.array(encoded)\n","\n","#data generator for batching\n","def datagen(paths, labels, batch_size=12, epochs=1):\n","    for _ in range(epochs):\n","        for i in range(0, len(paths), batch_size):\n","            batch_paths = paths[i:i + batch_size]\n","            batch_images = open_images(batch_paths)    #open & augment img\n","            batch_labels = labels[i:i + batch_size]\n","            batch_labels = encode_label(batch_labels)  # encode labels\n","            yield batch_images, batch_labels           #yield the batch\n","\n"],"metadata":{"id":"fpQW8CvY9OZQ","executionInfo":{"status":"ok","timestamp":1787458094055,"user_tz":-330,"elapsed":4,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"NEyd1bjLKnZx","executionInfo":{"status":"ok","timestamp":1787458096760,"user_tz":-330,"elapsed":26,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["# FIXED CLASS ORDER\n","class_names = [\n","    \"pituitary\",\n","    \"notumor\",\n","    \"meningioma\",\n","    \"glioma\"\n","]\n","\n","print(\"Class mapping:\")\n","for i, name in enumerate(class_names):\n","    print(f\"{i} → {name}\")\n","\n","\n","# TRAIN DATA\n","train_paths = []\n","train_labels = []\n","\n","# Use our fixed class order, NOT os.listdir() order\n","for label in class_names:\n","    for image in os.listdir(os.path.join(train_dir, label)):\n","        train_paths.append(\n","            os.path.join(train_dir, label, image)\n","        )\n","        train_labels.append(label)\n","\n","train_paths, train_labels = shuffle(\n","    train_paths,\n","    train_labels,\n","    random_state=42\n",")\n","\n","\n","# CREATE VALIDATION SET\n","from sklearn.model_selection import train_test_split\n","\n","train_paths, val_paths, train_labels, val_labels = train_test_split(\n","    train_paths,\n","    train_labels,\n","    test_size=0.15,\n","    random_state=42,\n","    stratify=train_labels\n",")\n","\n","\n","# TEST DATA\n","test_paths = []\n","test_labels = []\n","\n","# Use the SAME fixed class order\n","for label in class_names:\n","    for image in os.listdir(os.path.join(test_dir, label)):\n","        test_paths.append(\n","            os.path.join(test_dir, label, image)\n","        )\n","        test_labels.append(label)\n","\n","test_paths, test_labels = shuffle(\n","    test_paths,\n","    test_labels,\n","    random_state=42\n",")\n","\n","\n","# LABEL ENCODING\n","def encode_label(labels):\n","    encoded = [\n","        class_names.index(label)\n","        for label in labels\n","    ]\n","\n","    return np.array(encoded)"],"metadata":{"id":"L4XDljU4NnUS","executionInfo":{"status":"ok","timestamp":1787458142545,"user_tz":-330,"elapsed":20062,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5b040781-fca9-4166-9ea7-4ea4732a0a92"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Class mapping:\n","0 → pituitary\n","1 → notumor\n","2 → meningioma\n","3 → glioma\n"]}]},{"cell_type":"code","source":["print(class_names)\n","print(encode_label(class_names))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GimB_ISWvl25","executionInfo":{"status":"ok","timestamp":1787458142583,"user_tz":-330,"elapsed":49,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"1dd581ae-a945-4bb5-e5e4-7cf789caa186"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["['pituitary', 'notumor', 'meningioma', 'glioma']\n","[0 1 2 3]\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","train_paths, val_paths, train_labels, val_labels = train_test_split(\n","    train_paths,\n","    train_labels,\n","    test_size=0.15,\n","    random_state=42,\n","    stratify=train_labels\n",")"],"metadata":{"id":"Uxq6eYo_NjQK","executionInfo":{"status":"ok","timestamp":1787458142584,"user_tz":-330,"elapsed":4,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"mQ7OaRWYo89l"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# **Training of the model with first set of parameters**"],"metadata":{"id":"X3_kke9wKpkO"}},{"cell_type":"code","source":[],"metadata":{"id":"r5juY9eOs4Tm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# MODEL ARCHITECTURE - EfficientNetB0\n","\n","IMAGE_SIZE = 128\n","NUM_CLASSES = 4\n","\n","# Load EfficientNetB0 without the original ImageNet classifier\n","base_model = EfficientNetB0(\n","    input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),\n","    include_top=False,\n","    weights='imagenet'\n",")\n","\n","# STAGE 1:\n","# Freeze the entire EfficientNetB0 base\n","\n","for layer in base_model.layers:\n","    layer.trainable = False\n","\n","\n","# Build the classification model\n","model = Sequential()\n","\n","model.add(Input(shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))\n","\n","# EfficientNetB0 feature extractor\n","model.add(base_model)\n","\n","# Global Average Pooling instead of Flatten()\n","model.add(GlobalAveragePooling2D())\n","\n","# Classification head\n","model.add(Dense(256, activation='relu'))\n","model.add(BatchNormalization())\n","model.add(Dropout(0.4))\n","\n","model.add(Dense(128, activation='relu'))\n","model.add(BatchNormalization())\n","model.add(Dropout(0.3))\n","\n","# 4-class output\n","model.add(Dense(NUM_CLASSES, activation='softmax'))\n","\n","\n","# Compile Stage 1\n","model.compile(\n","    optimizer=Adam(learning_rate=1e-4),\n","    loss='sparse_categorical_crossentropy',\n","    metrics=['sparse_categorical_accuracy']\n",")\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":500},"id":"qo5v50EwKnOv","executionInfo":{"status":"ok","timestamp":1787458454600,"user_tz":-330,"elapsed":4105,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"edad5187-be3c-4e1e-b582-be770f56a46b"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n","\u001b[1m16705208/16705208\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ efficientnetb0 (\u001b[38;5;33mFunctional\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m1280\u001b[0m)     │     \u001b[38;5;34m4,049,571\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ global_average_pooling2d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1280\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n","│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m327,936\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m)              │           \u001b[38;5;34m516\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ efficientnetb0 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)     │     <span style=\"color: #00af00; text-decoration-color: #00af00\">4,049,571</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ global_average_pooling2d        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1280</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)        │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">327,936</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">516</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,412,455\u001b[0m (16.83 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,412,455</span> (16.83 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m362,116\u001b[0m (1.38 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">362,116</span> (1.38 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m4,050,339\u001b[0m (15.45 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,050,339</span> (15.45 MB)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["# EFFICIENTNETB0 - STAGE 1 TRAINING\n","\n","batch_size = 20\n","epochs = 20\n","\n","steps = len(train_paths) // batch_size\n","\n","\n","# Early stopping\n","early_stopping = tf.keras.callbacks.EarlyStopping(\n","    monitor='val_loss',\n","    patience=5,\n","    restore_best_weights=True,\n","    verbose=1\n",")\n","\n","\n","# Reduce learning rate when validation loss stops improving\n","reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n","    monitor='val_loss',\n","    factor=0.2,\n","    patience=2,\n","    min_lr=1e-7,\n","    verbose=1\n",")\n","\n","\n","# Save best EfficientNetB0 model\n","checkpoint = tf.keras.callbacks.ModelCheckpoint(\n","    'efficientnetb0_stage1_best.keras',\n","    monitor='val_loss',\n","    save_best_only=True,\n","    verbose=1\n",")\n","\n","\n","# Train\n","history = model.fit(\n","    datagen(\n","        train_paths,\n","        train_labels,\n","        batch_size=batch_size,\n","        epochs=epochs\n","    ),\n","\n","    epochs=epochs,\n","\n","    steps_per_epoch=steps,\n","\n","    validation_data=datagen(\n","        val_paths,\n","        val_labels,\n","        batch_size=batch_size,\n","        epochs=1\n","    ),\n","\n","    validation_steps=len(val_paths) // batch_size,\n","\n","    callbacks=[\n","        early_stopping,\n","        reduce_lr,\n","        checkpoint\n","    ]\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z4-uiyDlKnUm","outputId":"a915ea92-2561-4899-d3d1-d7f6feb48547","executionInfo":{"status":"ok","timestamp":1787460521810,"user_tz":-330,"elapsed":1963901,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7s/step - loss: 1.9831 - sparse_categorical_accuracy: 0.2477\n","Epoch 1: val_loss improved from None to 1.51455, saving model to efficientnetb0_stage1_best.keras\n","\n","Epoch 1: finished saving model to efficientnetb0_stage1_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1728s\u001b[0m 8s/step - loss: 1.8906 - sparse_categorical_accuracy: 0.2646 - val_loss: 1.5146 - val_sparse_categorical_accuracy: 0.2514 - learning_rate: 1.0000e-04\n","Epoch 2/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.7738 - sparse_categorical_accuracy: 0.2758\n","Epoch 2: val_loss did not improve from 1.51455\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 81ms/step - loss: 1.7661 - sparse_categorical_accuracy: 0.2712 - val_loss: 1.6306 - val_sparse_categorical_accuracy: 0.3000 - learning_rate: 1.0000e-04\n","Epoch 3/20\n","\u001b[1m  3/202\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m8s\u001b[0m 44ms/step - loss: 1.7671 - sparse_categorical_accuracy: 0.3391"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.13/dist-packages/keras/src/trainers/epoch_iterator.py:164: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n","  self._interrupted_warning()\n"]},{"output_type":"stream","name":"stdout","text":["\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.7383 - sparse_categorical_accuracy: 0.2890\n","Epoch 3: val_loss improved from 1.51455 to 1.45211, saving model to efficientnetb0_stage1_best.keras\n","\n","Epoch 3: finished saving model to efficientnetb0_stage1_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 85ms/step - loss: 1.7092 - sparse_categorical_accuracy: 0.2946 - val_loss: 1.4521 - val_sparse_categorical_accuracy: 0.3500 - learning_rate: 1.0000e-04\n","Epoch 4/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.6545 - sparse_categorical_accuracy: 0.3083\n","Epoch 4: val_loss did not improve from 1.45211\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 80ms/step - loss: 1.6508 - sparse_categorical_accuracy: 0.3075 - val_loss: 1.5628 - val_sparse_categorical_accuracy: 0.2750 - learning_rate: 1.0000e-04\n","Epoch 5/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.6658 - sparse_categorical_accuracy: 0.2737\n","Epoch 5: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n","\n","Epoch 5: val_loss did not improve from 1.45211\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 83ms/step - loss: 1.6417 - sparse_categorical_accuracy: 0.2864 - val_loss: 1.5094 - val_sparse_categorical_accuracy: 0.3250 - learning_rate: 1.0000e-04\n","Epoch 6/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 1.6283 - sparse_categorical_accuracy: 0.2999\n","Epoch 6: val_loss improved from 1.45211 to 1.33644, saving model to efficientnetb0_stage1_best.keras\n","\n","Epoch 6: finished saving model to efficientnetb0_stage1_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 90ms/step - loss: 1.6115 - sparse_categorical_accuracy: 0.3082 - val_loss: 1.3364 - val_sparse_categorical_accuracy: 0.3750 - learning_rate: 2.0000e-05\n","Epoch 7/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.6186 - sparse_categorical_accuracy: 0.3068\n","Epoch 7: val_loss did not improve from 1.33644\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 78ms/step - loss: 1.6017 - sparse_categorical_accuracy: 0.3077 - val_loss: 1.3399 - val_sparse_categorical_accuracy: 0.3000 - learning_rate: 2.0000e-05\n","Epoch 8/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.6277 - sparse_categorical_accuracy: 0.3003\n","Epoch 8: val_loss improved from 1.33644 to 1.32746, saving model to efficientnetb0_stage1_best.keras\n","\n","Epoch 8: finished saving model to efficientnetb0_stage1_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 88ms/step - loss: 1.6001 - sparse_categorical_accuracy: 0.3045 - val_loss: 1.3275 - val_sparse_categorical_accuracy: 0.4000 - learning_rate: 2.0000e-05\n","Epoch 9/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 1.5971 - sparse_categorical_accuracy: 0.3095\n","Epoch 9: val_loss did not improve from 1.32746\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 79ms/step - loss: 1.6003 - sparse_categorical_accuracy: 0.3127 - val_loss: 1.4716 - val_sparse_categorical_accuracy: 0.2250 - learning_rate: 2.0000e-05\n","Epoch 10/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.6010 - sparse_categorical_accuracy: 0.3096\n","Epoch 10: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.\n","\n","Epoch 10: val_loss did not improve from 1.32746\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 83ms/step - loss: 1.5874 - sparse_categorical_accuracy: 0.3127 - val_loss: 1.4175 - val_sparse_categorical_accuracy: 0.3000 - learning_rate: 2.0000e-05\n","Epoch 11/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.5966 - sparse_categorical_accuracy: 0.3104\n","Epoch 11: val_loss did not improve from 1.32746\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 84ms/step - loss: 1.5906 - sparse_categorical_accuracy: 0.3053 - val_loss: 1.3523 - val_sparse_categorical_accuracy: 0.4000 - learning_rate: 4.0000e-06\n","Epoch 12/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.5726 - sparse_categorical_accuracy: 0.3204\n","Epoch 12: ReduceLROnPlateau reducing learning rate to 7.999999979801942e-07.\n","\n","Epoch 12: val_loss did not improve from 1.32746\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 81ms/step - loss: 1.5766 - sparse_categorical_accuracy: 0.3157 - val_loss: 1.3719 - val_sparse_categorical_accuracy: 0.3750 - learning_rate: 4.0000e-06\n","Epoch 13/20\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.5861 - sparse_categorical_accuracy: 0.3145\n","Epoch 13: val_loss did not improve from 1.32746\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 81ms/step - loss: 1.5735 - sparse_categorical_accuracy: 0.3135 - val_loss: 1.3665 - val_sparse_categorical_accuracy: 0.4000 - learning_rate: 8.0000e-07\n","Epoch 13: early stopping\n","Restoring model weights from the end of the best epoch: 8.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"CPof__kiNy-j"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"Best validation accuracy:\")\n","print(max(history.history['val_sparse_categorical_accuracy']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ig4JaLoGXLxw","executionInfo":{"status":"ok","timestamp":1787461877887,"user_tz":-330,"elapsed":129,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"e1d28d79-5c88-4171-f46b-0c0d649d53a4"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Best validation accuracy:\n","0.4000000059604645\n"]}]},{"cell_type":"code","source":["print(\"Best validation loss:\")\n","print(min(history.history['val_loss']))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TXBzbim0XPyn","executionInfo":{"status":"ok","timestamp":1787461877911,"user_tz":-330,"elapsed":22,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"4c55c930-41f1-4fc2-c689-8d0fb86b2ba9"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Best validation loss:\n","1.3274562358856201\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(10,5))\n","plt.grid(True)\n","plt.plot(history.history['sparse_categorical_accuracy'], '.g-', linewidth=2)\n","plt.plot(history.history['loss'], '.r-', linewidth=2.5)\n","plt.title('Model Training History')\n","plt.xlabel('Epoch')\n","plt.ylabel('Value')\n","plt.xticks([x for x in range(epochs)])\n","plt.legend(['accuracy', 'loss'], loc='upper left', bbox_to_anchor=(1, 1))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"NoGrm8BxWI_X","executionInfo":{"status":"ok","timestamp":1787461878021,"user_tz":-330,"elapsed":65,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"433f8395-dd09-4871-b35c-96091d7a02d0"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","plt.plot(history.history['sparse_categorical_accuracy'], label='Training')\n","plt.plot(history.history['val_sparse_categorical_accuracy'], label='Validation')\n","\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"H5uHADMeXVUh","executionInfo":{"status":"ok","timestamp":1787461878421,"user_tz":-330,"elapsed":365,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"2ccf491d-fe7f-484e-8b69-d78ff2fca9b7"},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"skRQtWRQVy2d","executionInfo":{"status":"ok","timestamp":1787461878674,"user_tz":-330,"elapsed":36,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["test_images = open_images(test_paths)            #load and augment test images\n","test_labels_encoded = encode_label(test_labels)  # encode the test labels\n","#Predict on test data"],"metadata":{"id":"C9Al8JIrXxsb","executionInfo":{"status":"ok","timestamp":1787462488389,"user_tz":-330,"elapsed":608710,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["test_predictions = model.predict(test_images)\n","#predict using the trained model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PQ1XNQt1XyZL","executionInfo":{"status":"ok","timestamp":1787462505930,"user_tz":-330,"elapsed":17546,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"9d182df6-2328-49bd-9192-ef0571b47462"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 17ms/step\n"]}]},{"cell_type":"code","source":["print(classification_report(test_labels_encoded, np.argmax(test_predictions, axis=1)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ksy834n4YDX-","executionInfo":{"status":"ok","timestamp":1787462527064,"user_tz":-330,"elapsed":80,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"7955cfa2-d05e-4c64-cb16-c8b8acd53e1f"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.00      0.00      0.00       400\n","           1       0.52      0.56      0.53       400\n","           2       0.26      0.76      0.39       400\n","           3       0.25      0.01      0.01       400\n","\n","    accuracy                           0.33      1600\n","   macro avg       0.26      0.33      0.23      1600\n","weighted avg       0.26      0.33      0.23      1600\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n","/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n","/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"]}]},{"cell_type":"code","source":["conf_matrix = confusion_matrix(test_labels_encoded, np.argmax(test_predictions, axis=1))\n","print(conf_matrix)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GB4e4zE0YDNl","executionInfo":{"status":"ok","timestamp":1787462528485,"user_tz":-330,"elapsed":8,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"ce44c4bc-7467-46d8-8851-b4b870a5614c"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["[[  0  61 335   4]\n"," [  0 222 176   2]\n"," [  0  95 305   0]\n"," [  0  53 345   2]]\n"]}]},{"cell_type":"markdown","source":["# **Stage 2 fine-tuning**"],"metadata":{"id":"bRRjOC6rOKP4"}},{"cell_type":"code","source":["# STAGE 2 - FINE TUNING EfficientNetB0\n","\n","# Freeze everything first\n","for layer in base_model.layers:\n","    layer.trainable = False\n","\n","\n","# Unfreeze the final portion of EfficientNetB0\n","# Keep the majority of the pretrained backbone frozen\n","set_trainable = False\n","\n","for layer in base_model.layers:\n","\n","    if layer.name == 'block6a_expand_conv':\n","        set_trainable = True\n","\n","    if set_trainable:\n","        layer.trainable = True\n","\n","\n","# Keep Batch Normalization layers frozen\n","# This helps maintain the pretrained feature statistics\n","for layer in base_model.layers:\n","    if isinstance(layer, tf.keras.layers.BatchNormalization):\n","        layer.trainable = False\n","\n","\n","# Check which layers are trainable\n","print(\"\\nTrainable EfficientNetB0 layers:\")\n","\n","for layer in base_model.layers:\n","    if layer.trainable:\n","        print(layer.name, layer.trainable)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yDDIItDxOJR_","executionInfo":{"status":"ok","timestamp":1787462532777,"user_tz":-330,"elapsed":45,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"2fa2225b-f47a-428d-f208-8c1667afced7"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Trainable EfficientNetB0 layers:\n","block6a_expand_conv True\n","block6a_expand_activation True\n","block6a_dwconv_pad True\n","block6a_dwconv True\n","block6a_activation True\n","block6a_se_squeeze True\n","block6a_se_reshape True\n","block6a_se_reduce True\n","block6a_se_expand True\n","block6a_se_excite True\n","block6a_project_conv True\n","block6b_expand_conv True\n","block6b_expand_activation True\n","block6b_dwconv True\n","block6b_activation True\n","block6b_se_squeeze True\n","block6b_se_reshape True\n","block6b_se_reduce True\n","block6b_se_expand True\n","block6b_se_excite True\n","block6b_project_conv True\n","block6b_drop True\n","block6b_add True\n","block6c_expand_conv True\n","block6c_expand_activation True\n","block6c_dwconv True\n","block6c_activation True\n","block6c_se_squeeze True\n","block6c_se_reshape True\n","block6c_se_reduce True\n","block6c_se_expand True\n","block6c_se_excite True\n","block6c_project_conv True\n","block6c_drop True\n","block6c_add True\n","block6d_expand_conv True\n","block6d_expand_activation True\n","block6d_dwconv True\n","block6d_activation True\n","block6d_se_squeeze True\n","block6d_se_reshape True\n","block6d_se_reduce True\n","block6d_se_expand True\n","block6d_se_excite True\n","block6d_project_conv True\n","block6d_drop True\n","block6d_add True\n","block7a_expand_conv True\n","block7a_expand_activation True\n","block7a_dwconv True\n","block7a_activation True\n","block7a_se_squeeze True\n","block7a_se_reshape True\n","block7a_se_reduce True\n","block7a_se_expand True\n","block7a_se_excite True\n","block7a_project_conv True\n","top_conv True\n","top_activation True\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"CIIbG4Y-Oh0R"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# COMPILE FOR FINE-TUNING\n","\n","model.compile(\n","    optimizer=Adam(learning_rate=1e-5),\n","    loss='sparse_categorical_crossentropy',\n","    metrics=['sparse_categorical_accuracy']\n",")"],"metadata":{"id":"rxJy5ulQOmDi","executionInfo":{"status":"ok","timestamp":1787462565222,"user_tz":-330,"elapsed":39,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["# FINE-TUNE\n","\n","fine_tune_epochs = 15\n","\n","fine_tune_callbacks = [\n","\n","    tf.keras.callbacks.EarlyStopping(\n","        monitor='val_loss',\n","        patience=5,\n","        restore_best_weights=True,\n","        verbose=1\n","    ),\n","\n","    tf.keras.callbacks.ReduceLROnPlateau(\n","        monitor='val_loss',\n","        factor=0.2,\n","        patience=2,\n","        min_lr=1e-8,\n","        verbose=1\n","    ),\n","\n","    tf.keras.callbacks.ModelCheckpoint(\n","        'efficientnetb0_finetuned_best.keras',\n","        monitor='val_loss',\n","        save_best_only=True,\n","        verbose=1\n","    )\n","]\n","\n","\n","history_finetune = model.fit(\n","    datagen(\n","        train_paths,\n","        train_labels,\n","        batch_size=batch_size,\n","        epochs=fine_tune_epochs\n","    ),\n","\n","    epochs=fine_tune_epochs,\n","\n","    steps_per_epoch=len(train_paths) // batch_size,\n","\n","    validation_data=datagen(\n","        val_paths,\n","        val_labels,\n","        batch_size=batch_size,\n","        epochs=1\n","    ),\n","\n","    validation_steps=len(val_paths) // batch_size,\n","\n","    callbacks=fine_tune_callbacks\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIajgbqcOpLC","executionInfo":{"status":"ok","timestamp":1787462784582,"user_tz":-330,"elapsed":218513,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"c6fc5137-d741-4ac8-9884-00f11aff55a7"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 1.6136 - sparse_categorical_accuracy: 0.3067\n","Epoch 1: val_loss improved from None to 1.60793, saving model to efficientnetb0_finetuned_best.keras\n","\n","Epoch 1: finished saving model to efficientnetb0_finetuned_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 149ms/step - loss: 1.6076 - sparse_categorical_accuracy: 0.3074 - val_loss: 1.6079 - val_sparse_categorical_accuracy: 0.2629 - learning_rate: 1.0000e-05\n","Epoch 2/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.5868 - sparse_categorical_accuracy: 0.3195"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.13/dist-packages/keras/src/trainers/epoch_iterator.py:164: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.\n","  self._interrupted_warning()\n"]},{"output_type":"stream","name":"stdout","text":["\n","Epoch 2: val_loss improved from 1.60793 to 1.37947, saving model to efficientnetb0_finetuned_best.keras\n","\n","Epoch 2: finished saving model to efficientnetb0_finetuned_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 89ms/step - loss: 1.5742 - sparse_categorical_accuracy: 0.3264 - val_loss: 1.3795 - val_sparse_categorical_accuracy: 0.3250 - learning_rate: 1.0000e-05\n","Epoch 3/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.5699 - sparse_categorical_accuracy: 0.3293\n","Epoch 3: val_loss improved from 1.37947 to 1.32893, saving model to efficientnetb0_finetuned_best.keras\n","\n","Epoch 3: finished saving model to efficientnetb0_finetuned_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 89ms/step - loss: 1.5805 - sparse_categorical_accuracy: 0.3264 - val_loss: 1.3289 - val_sparse_categorical_accuracy: 0.3750 - learning_rate: 1.0000e-05\n","Epoch 4/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.5792 - sparse_categorical_accuracy: 0.3322\n","Epoch 4: val_loss improved from 1.32893 to 1.32118, saving model to efficientnetb0_finetuned_best.keras\n","\n","Epoch 4: finished saving model to efficientnetb0_finetuned_best.keras\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 90ms/step - loss: 1.5788 - sparse_categorical_accuracy: 0.3227 - val_loss: 1.3212 - val_sparse_categorical_accuracy: 0.3750 - learning_rate: 1.0000e-05\n","Epoch 5/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.5614 - sparse_categorical_accuracy: 0.3412\n","Epoch 5: val_loss did not improve from 1.32118\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 82ms/step - loss: 1.5451 - sparse_categorical_accuracy: 0.3433 - val_loss: 1.6880 - val_sparse_categorical_accuracy: 0.2250 - learning_rate: 1.0000e-05\n","Epoch 6/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 1.5798 - sparse_categorical_accuracy: 0.3246\n","Epoch 6: ReduceLROnPlateau reducing learning rate to 1.9999999494757505e-06.\n","\n","Epoch 6: val_loss did not improve from 1.32118\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 85ms/step - loss: 1.5624 - sparse_categorical_accuracy: 0.3328 - val_loss: 1.5880 - val_sparse_categorical_accuracy: 0.3750 - learning_rate: 1.0000e-05\n","Epoch 7/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.5348 - sparse_categorical_accuracy: 0.3286\n","Epoch 7: val_loss did not improve from 1.32118\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 83ms/step - loss: 1.5242 - sparse_categorical_accuracy: 0.3301 - val_loss: 1.3687 - val_sparse_categorical_accuracy: 0.3500 - learning_rate: 2.0000e-06\n","Epoch 8/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 1.5538 - sparse_categorical_accuracy: 0.3454\n","Epoch 8: ReduceLROnPlateau reducing learning rate to 3.999999989900971e-07.\n","\n","Epoch 8: val_loss did not improve from 1.32118\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 85ms/step - loss: 1.5347 - sparse_categorical_accuracy: 0.3485 - val_loss: 1.3270 - val_sparse_categorical_accuracy: 0.3000 - learning_rate: 2.0000e-06\n","Epoch 9/15\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.5505 - sparse_categorical_accuracy: 0.3387\n","Epoch 9: val_loss did not improve from 1.32118\n","\u001b[1m202/202\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 86ms/step - loss: 1.5320 - sparse_categorical_accuracy: 0.3378 - val_loss: 1.3479 - val_sparse_categorical_accuracy: 0.3000 - learning_rate: 4.0000e-07\n","Epoch 9: early stopping\n","Restoring model weights from the end of the best epoch: 4.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"NmIPmXqjaXhu"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["**Test**"],"metadata":{"id":"u1Yxil_2cHKC"}},{"cell_type":"code","source":["def load_test_images(paths):\n","    images = []\n","\n","    for path in paths:\n","        image = load_img(\n","            path,\n","            target_size=(IMAGE_SIZE, IMAGE_SIZE)\n","        )\n","\n","        image = np.array(image) / 255.0\n","        images.append(image)\n","\n","    return np.array(images)"],"metadata":{"id":"cJTtX_o4cGzK","executionInfo":{"status":"ok","timestamp":1787462828567,"user_tz":-330,"elapsed":45,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":27,"outputs":[]},{"cell_type":"code","source":["# Load test images WITHOUT augmentation\n","X_test = load_test_images(test_paths)\n","\n","# Encode test labels\n","y_test = encode_label(test_labels)\n","\n","print(\"Test images:\", X_test.shape)\n","print(\"Test labels:\", y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uWpwj6hHcJq6","executionInfo":{"status":"ok","timestamp":1787462834435,"user_tz":-330,"elapsed":4952,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"c4cb7aee-82be-4a5f-f3cf-26d589f813a9"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["Test images: (1600, 128, 128, 3)\n","Test labels: (1600,)\n"]}]},{"cell_type":"code","source":["y_pred_prob = model.predict(X_test)\n","\n","y_pred = np.argmax(y_pred_prob, axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QQHcTbH0cL0b","executionInfo":{"status":"ok","timestamp":1787462844469,"user_tz":-330,"elapsed":7813,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"3d9dc7b4-72cd-4a31-c0cd-3649302526bc"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"gXEt14IvcQ-7","executionInfo":{"status":"ok","timestamp":1787462844506,"user_tz":-330,"elapsed":36,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","cm = confusion_matrix(y_test, y_pred)\n","\n","print(cm)\n","\n","plt.figure(figsize=(7, 6))\n","\n","sns.heatmap(\n","    cm,\n","    annot=True,\n","    fmt='d',\n","    cmap='Blues'\n",")\n","\n","plt.xlabel(\"Predicted\")\n","plt.ylabel(\"Actual\")\n","plt.title(\"Confusion Matrix - Fine-Tuned VGG16\")\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":633},"id":"XjoV2kUqcTJr","executionInfo":{"status":"ok","timestamp":1787462844738,"user_tz":-330,"elapsed":233,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"c0c186c5-82b9-4f7f-a9e6-8b067a55f32b"},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["[[  0   0   0 400]\n"," [  0 200   0 200]\n"," [  0  57   0 343]\n"," [  0  35   0 365]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 700x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","class_names = sorted(os.listdir(train_dir))\n","\n","print(\n","    classification_report(\n","        y_test,\n","        y_pred,\n","        target_names=class_names\n","    )\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"101ZR_CPcZeM","executionInfo":{"status":"ok","timestamp":1787462844755,"user_tz":-330,"elapsed":15,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"f0df7aed-26c0-452a-e4ae-dd08467a0404"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","      glioma       0.00      0.00      0.00       400\n","  meningioma       0.68      0.50      0.58       400\n","     notumor       0.00      0.00      0.00       400\n","   pituitary       0.28      0.91      0.43       400\n","\n","    accuracy                           0.35      1600\n","   macro avg       0.24      0.35      0.25      1600\n","weighted avg       0.24      0.35      0.25      1600\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n","/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n","/usr/local/lib/python3.13/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score\n","\n","accuracy = accuracy_score(y_test, y_pred)\n","\n","print(f\"Test Accuracy: {accuracy:.4f}\")\n","print(f\"Test Accuracy: {accuracy * 100:.2f}%\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T6GbXqQMccuE","executionInfo":{"status":"ok","timestamp":1787462844766,"user_tz":-330,"elapsed":12,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"e83bb2a2-8e4d-4f29-e635-06affef8816a"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Accuracy: 0.3531\n","Test Accuracy: 35.31%\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"WwjdM_9Bcful","executionInfo":{"status":"ok","timestamp":1787462844990,"user_tz":-330,"elapsed":2,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import tensorflow as tf\n","from tensorflow.keras.models import load_model\n","from sklearn.metrics import accuracy_score\n","\n","# LOAD BOTH MODELS\n","\n","# Original VGG16 model\n","original_model = load_model(\"/content/drive/MyDrive/cancer-cnn/efficientnetb0_stage1_best.keras\")\n","\n","# Fine-tuned VGG16 model\n","finetuned_model = load_model(\"/content/drive/MyDrive/cancer-cnn/efficientnetb0_finetuned_best.keras\")\n","\n","# LOAD TEST DATA\n","X_test = load_test_images(test_paths)\n","y_test = encode_label(test_labels)\n","\n","# PREDICTIONS - ORIGINAL MODEL\n","original_prob = original_model.predict(\n","    X_test,\n","    verbose=1\n",")\n","\n","original_pred = np.argmax(\n","    original_prob,\n","    axis=1\n",")\n","\n","original_accuracy = accuracy_score(\n","    y_test,\n","    original_pred\n",")\n","\n","\n","# PREDICTIONS - FINE-TUNED MODEL\n","finetuned_prob = finetuned_model.predict(\n","    X_test,\n","    verbose=1\n",")\n","finetuned_pred = np.argmax(\n","    finetuned_prob,\n","    axis=1\n",")\n","finetuned_accuracy = accuracy_score(\n","    y_test,\n","    finetuned_pred\n",")\n","\n","\n","# PRINT RESULTS\n","print(\"        MODEL ACCURACY COMPARISON\")\n","print(\" \")\n","\n","print(\n","    f\"Original       : \"\n","    f\"{original_accuracy * 100:.2f}%\"\n",")\n","\n","print(\n","    f\"Fine-tuned     : \"\n","    f\"{finetuned_accuracy * 100:.2f}%\"\n",")\n","\n","print(\" \")\n","\n","improvement = (\n","    finetuned_accuracy - original_accuracy\n",") * 100\n","\n","print(\n","    f\"Improvement         : \"\n","    f\"{improvement:+.2f} percentage points\"\n",")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FUzxK7bvdLKC","executionInfo":{"status":"ok","timestamp":1787463257744,"user_tz":-330,"elapsed":30960,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"41e73cc2-ee23-46b0-cd0a-d2ad8300e23b"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 16ms/step\n","\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 20ms/step\n","        MODEL ACCURACY COMPARISON\n"," \n","Original VGG16      : 34.62%\n","Fine-tuned VGG16    : 35.31%\n"," \n","Improvement         : +0.69 percentage points\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"XOW2SUZ4fMqH","executionInfo":{"status":"ok","timestamp":1787462880748,"user_tz":-330,"elapsed":59,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":33,"outputs":[]},{"cell_type":"markdown","source":["**prediction system**"],"metadata":{"id":"V1CJw3VpneEK"}},{"cell_type":"code","source":["from tensorflow.keras.models import load_model\n","model = load_model('/content/drive/MyDrive/cancer-cnn/efficientnetb0_finetuned_best.keras')"],"metadata":{"id":"QOGO9Cg4fYaR","executionInfo":{"status":"ok","timestamp":1787462978892,"user_tz":-330,"elapsed":5977,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["# IMPORTANT:\n","# This mapping matches the output you observed:\n","# model output 0 -> pituitary\n","# model output 1 -> notumor\n","# model output  -> meningioma\n","# model output 3 -> glioma\n","\n","\n","class_names = [\n","    \"pituitary\",\n","    \"notumor\",\n","    \"meningioma\",\n","    \"glioma\"\n","]"],"metadata":{"id":"wo8iwPFOfYXu","executionInfo":{"status":"ok","timestamp":1787462981149,"user_tz":-330,"elapsed":2,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","from tensorflow.keras.utils import load_img, img_to_array\n","import numpy as np\n","\n","def predict_image(image_path, model):\n","\n","    # Load image\n","    image = load_img(\n","        image_path,\n","        target_size=(IMAGE_SIZE, IMAGE_SIZE)\n","    )\n","\n","    # Convert image to array\n","    image_array = img_to_array(image)\n","\n","    # Normalize\n","    image_array = image_array / 255.0\n","\n","    # Add batch dimension\n","    input_image = np.expand_dims(image_array, axis=0)\n","\n","    # Predict\n","    prediction = model.predict(input_image, verbose=0)[0]\n","\n","    predicted_index = np.argmax(prediction)\n","    predicted_class = class_names[predicted_index]\n","    confidence = prediction[predicted_index] * 100\n","\n","    # Display image\n","    plt.figure(figsize=(6, 6))\n","    plt.imshow(image)\n","    plt.axis(\"off\")\n","\n","    plt.title(\n","        f\"Prediction: {predicted_class}\\n\"\n","        f\"Confidence: {confidence:.2f}%\"\n","    )\n","\n","    plt.show()\n","\n","    # Print probabilities\n","    print(\"MRI PREDICTION\")\n","    print(\" \")\n","\n","    for i, class_name in enumerate(class_names):\n","        print(\n","            f\"{class_name:12s}: \"\n","            f\"{prediction[i] * 100:.2f}%\"\n","        )\n","\n","    print(\" \")\n","    print(f\"Prediction : {predicted_class}\")\n","    print(f\"Confidence : {confidence:.2f}%\")\n","\n","    return predicted_class, confidence"],"metadata":{"id":"rOBg6snommAE","executionInfo":{"status":"ok","timestamp":1787463264554,"user_tz":-330,"elapsed":2,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":42,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"qnPgpAXKmlzu","executionInfo":{"status":"ok","timestamp":1787462983049,"user_tz":-330,"elapsed":2,"user":{"displayName":"Abin K","userId":"04862970659921921970"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["predict_image(\n","    \"/content/drive/MyDrive/cancer-cnn/extracted data/Testing/glioma/Te-gl_131.jpg\",\n","    model\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":716},"id":"Jd3h7DCZfMgx","executionInfo":{"status":"ok","timestamp":1787462997683,"user_tz":-330,"elapsed":13968,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"b622bbc0-d40d-4413-bc6c-5eba6d14a903"},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["MRI PREDICTION\n"," \n","pituitary   : 23.11%\n","notumor     : 11.08%\n","meningioma  : 12.20%\n","glioma      : 53.61%\n"," \n","Prediction : glioma\n","Confidence : 53.61%\n"]},{"output_type":"execute_result","data":{"text/plain":["('glioma', np.float32(53.609467))"]},"metadata":{},"execution_count":37}]},{"cell_type":"code","source":["predict_image(\n","    \"/content/drive/MyDrive/cancer-cnn/extracted data/Testing/meningioma/Te-aug-me_18.jpg\",\n","    model\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":716},"id":"DF6fc_EqfyCU","executionInfo":{"status":"ok","timestamp":1787462997840,"user_tz":-330,"elapsed":159,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"2db98bef-298e-46b9-d7bd-f0521d59a487"},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["MRI PREDICTION\n"," \n","pituitary   : 18.21%\n","notumor     : 15.40%\n","meningioma  : 15.43%\n","glioma      : 50.96%\n"," \n","Prediction : glioma\n","Confidence : 50.96%\n"]},{"output_type":"execute_result","data":{"text/plain":["('glioma', np.float32(50.9642))"]},"metadata":{},"execution_count":38}]},{"cell_type":"code","source":["predict_image(\n","    \"/content/drive/MyDrive/cancer-cnn/extracted data/Testing/notumor/Te-no_127.jpg\",\n","    model\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":716},"id":"qYX-eDWAieBA","executionInfo":{"status":"ok","timestamp":1787462997994,"user_tz":-330,"elapsed":152,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"1227edba-9251-4817-c70d-11a84bb69e47"},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["MRI PREDICTION\n"," \n","pituitary   : 2.99%\n","notumor     : 89.75%\n","meningioma  : 4.36%\n","glioma      : 2.89%\n"," \n","Prediction : notumor\n","Confidence : 89.75%\n"]},{"output_type":"execute_result","data":{"text/plain":["('notumor', np.float32(89.754105))"]},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["predict_image(\n","    \"/content/drive/MyDrive/cancer-cnn/extracted data/Testing/pituitary/Te-pi_167.jpg\",\n","    model\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":716},"id":"lUbqlDdqiegI","executionInfo":{"status":"ok","timestamp":1787462998155,"user_tz":-330,"elapsed":159,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"966f5818-1fa5-496e-f0fc-d19b066be4e7"},"execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["MRI PREDICTION\n"," \n","pituitary   : 18.58%\n","notumor     : 24.84%\n","meningioma  : 16.46%\n","glioma      : 40.12%\n"," \n","Prediction : glioma\n","Confidence : 40.12%\n"]},{"output_type":"execute_result","data":{"text/plain":["('glioma', np.float32(40.12152))"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":[],"metadata":{"id":"fSuYzndkjHRk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(os.listdir(train_dir))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2m8tygzSqLYM","executionInfo":{"status":"ok","timestamp":1787388571855,"user_tz":-330,"elapsed":26,"user":{"displayName":"Abin K","userId":"04862970659921921970"}},"outputId":"2acdb560-92da-44a5-f861-291875150324"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['pituitary', 'notumor', 'meningioma', 'glioma']\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"j9MzDSuuqS7q"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}