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

# 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

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## Dataset Structure (Example)

Dataset/

├── NonDemented/

│ ├── image1.jpg

│ └── ...

├── MildDemented/

│ ├── image2.jpg

│ └── ...

├── VeryMildDemented/

│ └── ...