Hybrid D-OCapNet: Automated Multi-Class Alzheimer’s Disease Classification in Brain MRI Using Hybrid Dense Optimal Capsule Network

Abstract

Efficient detection of Alzheimer’s disease (AD) is challenging in medical image processing. Different methodologies are proposed for detecting AD at earlier stages, but certain demerits emerge, like less robust, less convergent, time-consuming, and lead over maximized losses. Hence the proposed research paper develops an efficient and automated deep learning-based AD detection using MRI image data. The initial step in AD classification is data acquisition which focuses on collecting brain MRI images from Alzheimer’s disease Neuroimaging Initiative (ADNI) Extracted Axial dataset and OASIS dataset. The gathered data are pre-processed through an Improved Median Filter (IMF), normalization is performed using the [0, 1] rescaling method, and skull stripping is done using Morphological Thresholding (MoT). The pre-processed images are fed into Multiview Fuzzy Clustering (MvFC) algorithm to segment the brain tissues as Gray Matter (GM), Cerebrospinal Fluid (CSF) and White Matter (WM) effectively. The process of Deep Feature Extraction, Multi-class Classification and loss optimization is performed using the Hybrid Dense Optimal Capsule Network (Hybrid D-OCapNet). The loss evaluated in the proposed Hybrid D-OCapNet model is optimized using the Modified Bald Eagle Search (M-BES) optimization algorithm. The simulation outcomes of training, testing and validation in AD classification are analyzed using MATLAB. The overall accuracy in classifying AD is 99.32%, sensitivity is 98.42%, specificity is 98.90%, precision is 98.93%, and F1 score is 98.44 for the ADNI dataset. The accuracy of 98.97%, the sensitivity of 98.31% and the F1 score of 98.39% are obtained for the OASIS dataset.