Diagnosis of Alzheimer's Disease: A Transfer Learning Method

Table of Contents

1. Overview
2. Key Features/Technologies
3. Datasets
4. Contributors

Overview

This project focuses on the task of image classification to diagnose Alzheimer's and dementia from medical MRIs. It involves a binary and a multiclass classification, with corresponding labels:

Binary:

• Healthy
• Dementia

Multiclass:

• healthy
• very mild dementia
• mild dementia
• moderate dementia

Key Features/Technologies

• Binary and multiclass classification of Alzheimer's and dementia.

• Five pretrained CNNs: AlexNet, ResNet101, ResNet50, GoogLeNet, InceptionResNetV2.

• Fine-tuning models on different datasets for cross validation.

• MATLAB

• Transfer Learning

  

Datasets

• OASIS (https://www.oasis-brains.org/)
• Alzheimer-MRI-dataset (https://www.kaggle.com/datasets/legendahmed/alzheimermridataset/metadata)
• ADNI (https://adni.loni.usc.edu/)

Contributors

• Andrea Loddo (https://github.com/andrealoddo)
• Cecilia Di Ruberto