Early Alzheimer's Detection and Classification using VGG Convolutional Neural Network and Systematic Data Augmentation using MR Images

Motivation: The use of different imaging tools at various hospitals results in varying contrast images. This fragmentation in healthcare prompted me to develop a personalized network that can be trained using hospital imaging database. Goal(s): The main goal of this project is to predict early stages of Alzheimer's Disease (AD) using Magnetic Resonance (MR) images. Approach: We applied convolutional neural network (CNN) to the T1 weighted images of AD, publicly available at https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images. The images were classified into four classes. F1 score and Area Under Curve (AUC) were calculated for the model after training. Results: We demonstrated F1 score of 99.60% and AUC 0.994. Impact: This model could be used to predict AD to other datasets that might help early detection of AD and subsequently improve treatment strategies. With various mice brain scan training, this network can also be used to aid AD researchers.