In Neuroscience research
The objective of study was to explore those brain areas that were affected at each stage during the progression of Alzheimer's disease and advancements in deep learning that grabbed the research community. Six affected brain areas were explored at mild cognitive impairment, four at first stage and six at each 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 percent accuracy for separating stage 1 Alzheimer's disease from mild cognitive impairment. 96.7 percent accuracy was acquired to distinguish stage 2 Alzheimer's disease from mild cognitive impairment, and stage 3 Alzheimer's disease was separated from mild cognitive impairment with an accuracy of 97.8 percent.
Ahmad Fayyaz, Javaid Muqaddas, Athar Muhammad, Shahzadi Samra
2023-Jan-19
Alzheimer’s disease, Binary classification, Brain regions, Deep learning algorithm, Independent component analysis, Mild cognitive impairment
