Diagnose Alzheimer's disease using RESNET50
A MATLAB - based deep learning project that classifies Alzheimer's disease form brain MRI images using transfer learnin with ResNet50
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# Alzheimer's Disease Classification using ResNet-50 (MATLAB)
This project demonstrates how to classify Alzheimer's disease stages using brain MRI images and a transfer learning approach based on a pre-trained ResNet-50 model. It is implemented in MATLAB using Deep Learning Toolbox.
## Project Overview
- Input: Brain MRI images organized in class-labeled folders
- Model: Pre-trained ResNet-50 (ImageNet)
- Learning: Transfer Learning + Data Augmentation
- Output: Multi-class classification (e.g., NonDemented, MildDemented, etc.)
- Visualization: Prediction results + ROC Curves
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## Key Features
### Data Preparation
- Uses `imageDatastore` for automatic label extraction from folder names
- Splits dataset into 70% training and 30% validation
- Converts grayscale to RGB images (`gray2rgb`)
### Model Customization (Transfer Learning)
- Loads ResNet-50 and removes original classification layers (`fc1000`)
- Adds new fully connected, softmax, and classification layers
- Connects new layers using `layerGraph`
### Data Augmentation
Enhances generalization performance using:
- Random X/Y reflection
- Random translation (±50 pixels)
- Random rotation (±30 degrees)
### Training Setup
- Optimizer: Stochastic Gradient Descent with Momentum (SGDM)
- Mini-batch size: 8
- Epochs: 10
- Learning rate: 0.001
- Includes real-time training plot
### Evaluation & Visualization
- Displays random prediction results (3×3 subplot)
- Generates ROC curves with AUC for each class
---
## Dataset Structure (Example)
Dataset/
├── NonDemented/
│ ├── image1.jpg
│ └── ...
├── MildDemented/
│ ├── image2.jpg
│ └── ...
├── VeryMildDemented/
│ └── ...
General Information
- Version 1.0.0 (2.28 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0 |
