A Short Survey on Computer-Aided Diagnosis of Alzheimer's Disease: Unsupervised Learning, Transfer Learning, and Other Machine Learning Methods

Abstract

Review A Short Survey on Computer-Aided Diagnosis of Alzheimer’s Disease: Unsupervised Learning, Transfer Learning, and Other Machine Learning Methods Si-Yuan Lu School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China Received: 15 April 2024; Revised: 7 May 2024; Accepted: 14 May 2024; Published: 31 May 2024   Abstract: Alzheimer’s Disease (AD) is a neurodegenerative disorder, which is irreversible and incurable. Early diagnosis plays a significant role in controlling the progression of AD and improving the patient’s quality of life. Computer-aided diagnosis (CAD) methods have shown great potential to assist doctors in analyzing medical data, such as magnetic resonance images, positron emission tomography, and mini-mental state examination. Contributed by the advanced deep learning models, predictions of CAD methods for AD are becoming more and more accurate, which can provide a reference and verification for manual screening. In this paper, a short survey on the application of recent CAD methods in AD detection is presented. The advantages and drawbacks of these methods are discussed in detail, especially the methods based on convolutional neural networks, and the future research directions are summarized subsequently. With this survey, we hope to promote the development of CAD for early detection of AD.