README.md

---
title: MRI_tumor_classification
app_file: app.py
sdk: gradio
sdk_version: 6.14.0
---
# Mr.9xKlug

## MRI Tumor Classification Model

This project uses a ResNet18 model to classify MRI images into four categories: No Tumor, Glioma, Meningioma, and Pituitary.

### Live Demo

Try out the model with our interactive demo:

[![Gradio](https://img.shields.io/badge/Gradio-Live%20Demo-blue)](https://huggingface.co/spaces/ubuti/MRI_tumor_classification)


### How to Use

1. Click on the *Live Demo* above.
2. Upload an MRI image or use one of the provided examples.
3. The model will classify the image and provide probabilities for each tumor type.

### Local Installation

If you want to run the model locally:

1. Clone this repository
2. Install dependencies: `conda env create -f environment.yml`.
3. Run the Gradio interface: `python3 -u app.py`

### Model Details

- Architecture: torchvision.models.resnet18 was used and initialised with the weights and configuration of [BehradG](https://huggingface.co/BehradG/resnet-18-finetuned-MRI-Brain/tree/main).
- Training Data: Two datasets available on **kaggle** where merged [1](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri), [2](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset).
- Performance: Model exceeds $97%$ accuracy.

### Contact

Other authors contribution in form of dataset contribution and model pretraining is very much appreciated!