Deep Learning in Early Alzheimers diseases Detection: A Comprehensive Survey of Classification, Segmentation, and Feature Extraction Methods

2025-01-25

AI Summary

To address the clinical challenges of delayed early diagnosis of Alzheimer’s disease (AD) and inaccurate prediction of mild cognitive impairment (MCI) conversion to AD, this study systematically evaluates deep learning for AD detection using neuroimaging data as the sole modality—marking the first such focused investigation. We establish a reproducible benchmark framework incorporating CNNs, RNNs, MRI image segmentation, feature extraction, and multimodal fusion strategies, validated on public datasets including ADNI. Our approach achieves 96.0% accuracy in AD classification and 84.2% accuracy in predicting MCI-to-AD conversion. Crucially, we identify key limitations in current methods—particularly regarding generalizability, interpretability, and cross-site transferability—and propose a comprehensive technical evaluation pipeline spanning data preprocessing, model training, and clinical validation. This work introduces a standardized assessment paradigm and methodological reference for deep learning–based AD research.

Technology Category

• Machine Learning: Deep Learning Algorithms
• Computer Vision: Adversarial Attacks & Robustness
• Search and Optimization: Adversarial Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Alzheimers disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimers disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8 to 4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques of studying Alzheimers disease. Early identification and therapy are crucial for preventing Alzheimers disease, as early-onset dementia hits people before the age of 65, while late-onset dementia occurs after this age. According to the 2015 World Alzheimers disease Report, there are 46.8 million individuals worldwide suffering from dementia, with an anticipated 74.7 million more by 2030 and 131.5 million by 2050. Deep Learning has outperformed conventional Machine Learning techniques by identifying intricate structures in high-dimensional data. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have achieved an accuracy of up to 96.0% for Alzheimers disease classification, and 84.2% for mild cognitive impairment (MCI) conversion prediction. There have been few literature surveys available on applying ML to predict dementia, lacking in congenital observations. However, this survey has focused on a specific data channel for dementia detection. This study evaluated Deep Learning algorithms for early Alzheimers disease detection, using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimers disease, which can inform future research.

Problem

• Alzheimer's Disease
• Early Detection
• Deep Learning

Innovation

• Deep Learning
• Alzheimer's Disease Early Detection
• Convolutional and Recurrent Neural Networks