Abstract
Alzheimer's Disease (AD) is a devastating neurodegenerative brain disorder with no cure. An early identification helps patients with AD sustain a normal living. We have outlined machine learning (ML) methodologies with different schemes of feature extraction to synergize complementary and correlated characteristics of data acquired from multiple modalities of neuroimaging. A variety of feature selection, scaling, and fusion methodologies along with confronted challenges are elaborated for designing an ML-based AD diagnosis system. Additionally, thematic analysis has been provided to compare the ML workflow for possible diagnostic solutions. This comprehensive report adds value to the further advancement of computer-aided early diagnosis system based on multi-modal neuroimaging data from patients with AD.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
