Deep Learning Techniques for Alzheimer's Disease Detection

Abstract

Alzheimer's disease (AD) is a common chronic disorder with a high incidence rate that disproportionately affects elderly people. Deep learning (DL) has been increasingly popular in recent years, resulting in notable developments and innovations in medical imaging. Consequently, deep learning has emerged as the preferred approach for analyzing medical visuals, particularly in the realm of Alzheimer's disease detection. In this chapter, the authors performed a comparative analysis of various DL models such as convolutional neural networks, DenseNet, ResNet, EfficientNet, etc., which have shown some groundbreaking results for AD disease detection. Also, they focused on investigating data collection and feature extraction techniques pertinent to AD. In addition, they further discussed briefly the deep learning models for AD detection. This not only increases hope for the advancement of AD research and therapy but also highlights how deep learning can revolutionize the field of medical image analysis and illness identification.