MCSO: MANTA CROW SEARCH OPTIMIZATION-ENABLED ACTOR CRITIC NEURAL NETWORK FOR ALZHEIMER CLASSIFICATION USING 3D MRI IMAGES

Abstract

A form of dementia is Alzheimer’s syndrome, also known as a neurodegenerative syndrome, damages the neuron cells in a selective mode. The count of patients that is increased gradually here by the disease is considered a worldwide concern, which may cause death in more cases. The rapid and accurate detection and categorization of Alzheimer’s disease have obtained enormous awareness from researchers due to the studies of a deep model. However, the productive detection and categorization of Alzheimer’s disease with reliable biomarkers are more challenging tasks. In this paper, Manta crow search optimization (MCSO)-based Actor Critic Neural Network (ACNN) is devised for the Alzheimer classification process. The Region of Interest (ROI) is extracted based on the thresholding process, and the preprocessing is done using the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Moreover, the Sparse Fuzzy C-means (Sparse FCM) model has been in employment to segment preprocessed images. The feature extraction is a productive process for classifying Alzheimer’s disease. The ACNN classifier is utilized for performing Alzheimer’s disease classification process. Furthermore, the ACNN classifier is skilled by a devised optimization algorithm named the MCSO model. The projected MCSO-based ACNN outperformed other existing techniques with a testing accuracy of 0.9295, sensitivity of 0.9378, and specificity of 0.9354.