Emerging Trends in Deep Learning for Early Alzheimer's Disease Diagnosis and Classification: A Comprehensive Review

Abstract

Alzheimer's Disease (AD), a progressive neurodegenerative disorder, manifests as cognitive decline and memory loss, significantly impacting individuals' lives and healthcare systems globally. Early diagnosis and intervention are crucial for improving patient outcomes and managing the disease effectively. Recent advancements in deep learning (DL) have shown substantial promise in medical image classification for early AD diagnosis. This survey evaluates state-of-the-art DL techniques, including hybrid models, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), applied across imaging modalities such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). It emphasizes their performance, accuracy, and computational efficiency while addressing critical challenges like the need for large annotated datasets, overfitting, and model interpretability. Furthermore, the survey explores how DL could revolutionize AD diagnosis and identifies future research directions to bridge existing gaps, aiming to improve early detection and personalized diagnostic approaches for individuals with AD.