Abstract
Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine learning methods have surfaced to diagnose Alzheimer's disease. Resting-state fMRI is a neuroimaging modality that has been widely utilized to study brain activity related to neurodegenerative diseases. In literature, the previous studies are limited to the binary classification of Alzheimer's disease and Mild Cognitive Impairment. The application of computer-aided diagnosis for the numerous advancing phases of Alzheimer's disease, on the other hand, remains understudied. This research analyzes and presents methods for multi-label classification of six Alzheimer's stages using rs-fMRI and deep learning. The proposed model solves the multi-class classification problem by extracting the brain's functional connectivity networks from rs-fMRI data and employing two deep learning approaches, Stacked Sparse Autoencoder and Brain Connectivity Graph Convolutional Network. The suggested models' results were assessed using the k-fold cross-validation approach, and an average accuracy of 77.13% and 84.03% was reached for multi-label classification using Stacked Sparse Autoencoders and Brain Connectivity Based Convolutional Network, respectively. An analysis of brain regions was also performed by using the network's learned weights, leading to the conclusion that the precentral gyrus, frontal gyrus, lingual gyrus, and supplementary motor area are the significant brain regions of interest.
•The study presents a framework for multi-label classification of six Alzheimer's disease stages using state-of-the-art deep learning methodology by extracting the brain's functional connectivity networks from the rs-fMRI.•The work is exemplary for Alzheimer's disease diagnosis using resting-state fMRI in order to study brain activity related to neurodegeneration.•A novel pipeline is presented to process rs-fMRI data was used to extract functional connectivity maps from a benchmark dataset on Alzheimer's disease.•State-of-the-art two deep learning methods are applied, and the performance of the models is evaluated using cross-validation, where results are better than other methods.•An analysis of brain regions has been performed using the network's learned weights, leading to the identification of the significant brain regions of interest.