Unveiling Alzheimer’s Disease Early: A Comprehensive Review of Machine Learning and Imaging Techniques

Abstract

Alzheimer’s disease (AD) represents a growing global health concern, emphasizing the urgent need for early detection and intervention strategies. This review article aims to provide a comprehensive analysis of the pivotal role that machine learning (ML) and advanced imaging techniques play in the early identification of AD. The study delves into an extensive array of methodologies, encompassing visual biomarkers, datasets, imaging modalities, and evaluation metrics essential for AD detection. The investigation encompasses diverse ML techniques, starting with pre-processing steps and extending to various data types, feature extractor models, and both conventional and deep learning algorithms. Highlighting the significance of Convolutional Neural Networks, Autoencoders, and Transfer Learning, the review assesses their efficacy in AD diagnosis. The findings underscore the intricate challenges and opportunities within the realm of AD detection. Notably, the integration of ML and imaging methods yields promising results in distinguishing AD patterns from healthy brain states. Robust models and algorithms demonstrated notable accuracy, sensitivity, and specificity when confronted with diverse datasets, thus paving the way for early AD identification. In conclusion, this review advocates for the pivotal role of ML and advanced imaging techniques in revolutionizing AD diagnosis. The amalgamation of these approaches holds immense potential for unveiling AD at its nascent stages, enabling timely therapeutic interventions and personalized patient care. Ultimately, this synthesis of biomedical research signifies a transformative leap towards addressing the pressing global issue of Alzheimer’s disease.