An efficient Alzheimer's disease prediction based on MEPC-SSC segmentation and momentum geo-transient MLPs

Abstract

A decline in cognitive functioning of the brain termed Alzheimer's Disease (AD) is an irremediable progressive brain disorder, which has no corroborated disease-modifying treatment. Therefore, to slow or avoid disease progression, a greater endeavour has been made to develop techniques for earlier detection, particularly at pre-symptomatic stages. To predict AD, several strategies have been developed. Nevertheless, it is still challenging to predict AD by classifying them into AD, Mild Cognitive Impairment (MCI), along with Normal Control (NC) regarding larger features. By utilizing the Momentum Golden Eagle Optimizer-centric Transient Multi-Layer Perceptron network (Momentum GEO-Transient MLP), an effectual AD prediction technique has been proposed to trounce the aforementioned issues. Firstly, the input images are supplied for post-processing. In post-processing, by employing Patch Wise L1 Norm (PWL1N), the image resizing along with noise removal is engendered. Then, by utilizing Truncate Intensity Based Operation (TIBO) from the post-processed images, the unwanted brain parts are taken away. Next, the skull-stripped images are pre-processed. In this, by deploying Carnot Cycle Entropy-centric Global and Local technique (c2EBGAL), the images are normalized along with ameliorated. Afterward, by implementing Modified Emperor Penguins Colony-centered Sparse Subspace Clustering (MEPC-SSC), the pre-processed images are segmented. Then, for extracting the features, the segmented images are utilized; subsequently, the features being extracted are fed to the Momentum GEO-Transient MLPs.For transferring images fromMRI into more compact higher-level features, this system is wielded for fusing features from diverse layers. The parameters, which minimize the computation complexity, are decreased. For AD classification, the proposed technique is analogized to the prevailing methodologies regardingaccuracy, sensitivity, specificity et cetera along with acquired enhanced outcomes. Thus, the proposed system is apt for the AD diagnosis.