Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease

Chitradevi Dhakhinamoorthy, Sathish Kumar Mani, Sandeep Kumar Mathivanan, Senthilkumar Mohan, Prabhu Jayagopal, Saurav Mallik () and Hong Qin ()
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Chitradevi Dhakhinamoorthy: Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai 600016, India
Sathish Kumar Mani: Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai 600016, India
Sandeep Kumar Mathivanan: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Senthilkumar Mohan: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Prabhu Jayagopal: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Saurav Mallik: Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Hong Qin: Department of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA

Mathematics, 2023, vol. 11, issue 5, 1-17

Abstract: In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.

Keywords: Alzheimer’s disease (AD); brain sub regions; deep learning (DL); metaheuristic optimization techniques; Mini-Mental State Examination (MMSE) score (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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