Deep Learning in Early Alzheimer's Disease's Detection: A Comprehensive Survey of Classification, Segmentation and Feature Extraction Methods

Abstract

Alzheimer's disease is a deadly neurological condition, impairing important memory and brain functions. Alzheimer's disease promotes brain shrinkage, ultimately leading to dementia. Dementia diagnosis typically takes 2.8-4.4 years after the first clinical indication. Advancements in computing and information technology have led to many techniques for studying Alzheimer's disease. Early identification and therapy are crucial for preventing Alzheimer's 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 Alzheimer's 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 Alzheimer's 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 Alzheimer's disease detection using openly accessible datasets, feature segmentation, and classification methods. This article also has identified research gaps and limits in detecting Alzheimer's disease, which can inform future research.