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supakornn/Brain-Tumor-Classification

sourceHugging Facemitupdated 5mo agoView on Hugging Face
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Brain Tumor Classification

CNN-based classification of brain tumor types from MRI scans.

Dataset

Source: Brain Tumor MRI Dataset (Kaggle)

4 classes: glioma, meningioma, notumor, pituitary — each split into Training/ and Testing/.

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Model Architecture

SimpleCNN with 10.7M parameters:

  • —3 Convolutional blocks (32, 64, 128 filters)
  • —Batch Normalization + Dropout
  • —2 Dense layers (256, 128 neurons)
  • —Softmax output (4 classes)

Results

ConfigurationAccuracyLossChange
Baseline42.39%3.2599-
Fine-tuned46.70%2.8472+4.31%

Fine-tuned hyperparameters: LR=0.0005, Batch=16, Epochs=40

Per-Class Performance (Fine-tuned)

ClassPrecisionRecallF1-Score
Glioma1.000.140.24
Meningioma1.000.210.35
No Tumor0.370.920.53
Pituitary0.540.660.59

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Usage

python
from tensorflow import keras

model = keras.models.load_model("model/SimpleCNN_best.h5")

GitHub

![GitHub](https://github.com/supakornn/brain-tumor-classification)

Limitations

  • —Low accuracy (46.70%) insufficient for clinical use
  • —Poor glioma detection (14% recall)
  • —Simple architecture inadequate for medical imaging

License

MIT License — Educational and research purposes.