A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data

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.