Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer's Disease Using Machine Learning Approaches.

This study sought to investigate how different brain regions are affected by Alzheimer's disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer's disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer's disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each.

Determination of affected brain regions at various stages of Alzheimer's disease.

The objective of study was to explore those brain areas that were affected at each stage during the progression of Alzheimer's disease (AD). Six affected brain areas were explored at mild cognitive impairment, four at first stage and six at each of second and third stage of Alzheimer's disease. The common brain regions among these stages were cuneus, precuneus, calcarine cortex, middle frontal gyrus, superior frontal gyrus, and frontal superior medial gyrus. The fMRI data at the resting state of 18 AD patients who were converted from MCI to stage 3 of Alzheimer's were taken from ADNI public source database. Among these patients, there were ten males and eight females. Independent component analysis was used to explore affected brain regions and an algorithm based on deep learning convolutional neural network was proposed for binary classification among the stages of Alzheimer's disease. The proposed CNN model delivered 94.6 % accuracy for separating stage 1 of Alzheimer's disease from mild cognitive impairment. 96.7 % accuracy was acquired to distinguish stage 2 of Alzheimer's disease from mild cognitive impairment, and stage 3 of Alzheimer's disease was separated from mild cognitive impairment with an accuracy of 97.8 %.