This project focuses on classifying brain tumors from MRI images using a deep learning model based on the EfficientNet B3 architecture. The goal is to assist in the early and accurate diagnosis of brain tumors, contributing positively to treatment outcomes.

Key Features

• Utilizes EfficientNet B3 with a slightly altered architecture for brain tumor classification.
• Implements the data preprocessing steps needed to leverage the MRI image data.
• Employs data augmentation methods to enhance the model's robustness.
• Performs hyperparameter tuning using Ray Tune to optimize the model's training.
• Includes a comprehensive assessment of the model's performance on unseen data.

Getting Started

Prerequisites

• Python 3.8+
• PyTorch, torchvision, Ray, timm, and other required libraries listed in requirements.txt.

Installation

1. Clone the repository:

   git clone https://github.com/AnnaTz/brain-tumor-mri-classification
   

2. Install the dependencies:

   pip install -r requirements.txt
   

Running the Project

Navigate to the project directory and launch the Jupyter notebook:

jupyter notebook mri_classification.ipynb

Follow the notebook's content for detailed steps on data preprocessing, model training, and evaluation.

References

• Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Link to the paper.
• Brain Tumor Classification (MRI) dataset on Kaggle: Link to the dataset.