A deep learning–based system for classifying Alzheimer’s Disease (AD) stages using structural MRI scans. This project uses a fine-tuned VGG16 CNN, ResNet50 architecture to classify MRI images into four stages of Alzheimer’s Disease.
This project implements a transfer-learning pipeline for multi-class Alzheimer’s Disease classification using MRI images. The model classifies images into:
The model uses preprocessing, augmentation, dropout, and early stopping to improve robustness and reduce overfitting.
The model is built on VGG16 (ImageNet pre-trained) with the following modifications:
Removed fully connected top layers
Added Global Average Pooling
Added Dense layers with ReLU
Added Dropout for regularization
Final Softmax layer for 4-class classification
This architecture provides a strong balance between accuracy and interpretability.
Strong performance in detecting Non-Demented and Very Mild Demented categories
Some confusion between Mild and Moderate Demented (expected due to overlapping MRI features)
Training/validation curves indicate reduced overfitting thanks to regularization
Overall, the model delivers promising accuracy on MRI-based Alzheimer’s classification.
Test advanced deep learning architectures (DenseNet, ViT)
Add Grad-CAM heatmaps for model explainability
Explore multi-modal learning by integrating MRI + clinical data
Deploy as a web-based diagnostic tool for clinicians
Suk et al., 2015 – Deep learning–based features for AD/MCI classification
Korolev et al., 2017 – CNNs for 3D brain MRI classification
Wang et al., 2018 – Transfer learning for Alzheimer’s diagnosis
Marcus et al., 2007 – OASIS MRI dataset
Jack et al., 2008 – ADNI MRI methods