Parkinson's MRI Classification Using Deep Learning

This project uses transfer learning techniques to classify patients with Parkinson's disease based compresing 3D images into 2D using CCNs and leveraging pre-trained convolutional neural networks (CNNs), the project aims to deliver a high-performance solution for early and accurate diagnosis of Parkinson's disease.

Key Features

• MRI Preprocessing Pipeline: Ensures clean and standardized input for model training using FSL.
• Transfer Learning Approach: Fine-tunes pre-trained 2D CNN architectures (e.g., ResNet, EfficientNet, or VGG) for Parkinson’s MRI slice classification.
• Deep Learning Architecture: Custom or fine-tuned CNN models optimized for MRI data.
• Comprehensive Evaluation: Metrics including accuracy, sensitivity, specificity, and ROC-AUC.
• Reproducible Research: Fully documented code and configuration files for easy replication.

Tools and Technologies

• Python (PyTorch)
• nibabel for MRI preprocessing
• Visualization libraries: Matplotlib, Seaborn
• DICOM/NIfTI handling