Deep Learning for Early Alzheimer Disease Detection with MRI Scans

TL;DR

This paper compares deep learning models like CNN, Bayesian CNN, and U-net for early Alzheimer's detection using MRI scans, aiming to improve diagnostic accuracy and efficiency in neurodegenerative disease management.

Contribution

It provides a comparative analysis of multiple deep learning models on MRI data for Alzheimer's diagnosis, addressing data imbalance and evaluating robustness.

Findings

Bayesian CNN showed higher robustness to data imbalance.

U-net achieved superior segmentation accuracy.

All models improved diagnostic speed compared to traditional methods.

Abstract

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.