README.md

metadata

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)

	precision	recall	f1-score	support
glioma	0.94	0.97	0.95	300
meningioma	0.95	0.93	0.94	306
notumor	0.99	0.99	0.99	405
pituitary	0.99	0.99	0.99	300
accuracy			0.97	1311

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