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

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