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Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review by Rahul Sharma; Tripti Goel; M. Tanveer; C. T. Lin; R. Murugan is a Computer Science article available to read on EtoBox.

What is Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review about?

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder and the most common cause of Dementia. Neuroimaging analyses such as T1 weighted Magnetic Resonance Imaging, Positron Emission Tomography, and the deep learning (DL) approaches have attracted researchers for automated AD diagnosis in the early stages. Therefore, a review is required to understand DL algorithms to develop more efficient AD diagnosis methods. This paper discusses a detailed review of automated early AD diagnosis using DL methods published from 2009 to 2022. The novelties of this paper include: 1) introducing popular imaging modalities, 2) discussing early biomarkers for AD diagnosis using neuroimaging scans, 3) reviewing the popular online available datasets widely used, 4) systematically describing the various DL algorithms for accurate and early assessment of AD, 5) discussion on advantages & limitations of the DL-based model for AD diagnosis, and 6) provides an outlook toward future trends derived from our critical assessment.

Who reads Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review?

Author

Rahul Sharma; Tripti Goel; M. Tanveer; C. T. Lin; R. Murugan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)