Alzheimer's Disease Stage Detection Using Deep Learning
This repository contains the implementation of a deep learning model designed to detect various stages of Alzheimer's disease using MRI brain scans. The project employs a transfer learning approach, utilizing the EfficientNetB0 architecture, adapted to classify images into four distinct stages: nondemented, very mild, mild, and moderate dementia. Built with PyTorch, the model is trained on a structured dataset of MRI images and provides a robust framework for early and accurate diagnosis of Alzheimer’s progression.
- Model Architecture: Utilizes the pre-trained EfficientNetB0 model, fine-tuned for the specific task of classifying Alzheimer's disease stages from brain MRIs.
- Data Processing: Includes comprehensive preprocessing and augmentation techniques to optimize model training and performance.
- API Integration: Features a Flask-based REST API that enables users to upload MRI images and receive diagnostic predictions, facilitating easy interaction with the model.
- Containerization: Docker support for easy setup and deployment, ensuring consistency across different environments.
- Evaluation Metrics: Implements various metrics for thorough evaluation and validation of the model, ensuring high accuracy and reliability.
/model: Contains the model definitions and state./data: Dataset directory with train and test splits./utils: Utility functions for data loading and transformations.main.py: Entry point for running training sessions.train.py: Script for model training.evaluate.py: Script for model evaluation and performance metrics.
- To provide a reliable tool for early detection of Alzheimer's disease stages, aiding in better management and treatment planning.
- To contribute to the ongoing research in medical AI by demonstrating the application of advanced machine learning techniques in healthcare diagnostics.
- To offer an accessible platform for further development and validation by the research community, healthcare professionals, and technology enthusiasts.
This project is intended for educational and research purposes, aiming to bridge the gap between medical imaging and machine learning technologies.