Amr2272/Brain_Tumor_MRI

license: mit language: en library_name: pytorch tags:

• —image-classification
• —pytorch
• —brain-tumor
• —resnet
• —medical-imaging ---

Brain Tumor MRI Classification Model (ResNet50)

This is a ResNet50-based image classification model fine-tuned to classify brain tumor MRIs into four categories. This model was trained as part of a project and achieved high accuracy on the test set.

Model Description

This model was trained on the Brain Tumor MRI Dataset. It uses a pre-trained ResNet50 architecture from torchvision, where the final layers were fine-tuned for the specific task of identifying brain tumors from MRI scans.

The model classifies images into the following categories:

• —glioma
• —meningioma
• —notumor
• —pituitary

Training Procedure

• —Architecture: ResNet50 (Fine-Tuning)
• —Optimizer: Adam with differential learning rates
• —Loss Function: CrossEntropyLoss
• —Epochs: 15
• —Scheduler: CosineAnnealingLR

Evaluation Results

The model achieved excellent performance, demonstrating its effectiveness on this dataset.

• —Best Validation Accuracy: 97.29%
• —Final Test Set Accuracy: 96.95%

Classification Report (Test Set)

Disclaimer: This model is intended for educational and research purposes only and should not be used for medical diagnosis.