Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease
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 a...
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MDPI AG
2023-02-01
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| author | Chitradevi Dhakhinamoorthy Sathish Kumar Mani Sandeep Kumar Mathivanan Senthilkumar Mohan Prabhu Jayagopal Saurav Mallik Hong Qin |
| author_facet | Chitradevi Dhakhinamoorthy Sathish Kumar Mani Sandeep Kumar Mathivanan Senthilkumar Mohan Prabhu Jayagopal Saurav Mallik Hong Qin |
| author_sort | Chitradevi Dhakhinamoorthy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:18:45Z |
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| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T07:18:45Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a4ab0f980b084eafb53f7f73320190072023-11-17T08:08:42ZengMDPI AGMathematics2227-73902023-02-01115113610.3390/math11051136Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s DiseaseChitradevi Dhakhinamoorthy0Sathish Kumar Mani1Sandeep Kumar Mathivanan2Senthilkumar Mohan3Prabhu Jayagopal4Saurav Mallik5Hong Qin6Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai 600016, IndiaDepartment of Computer Applications, Hindustan Institute of Technology and Science, Chennai 600016, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USADepartment of Computer Science and Engineering, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USAIn 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.https://www.mdpi.com/2227-7390/11/5/1136Alzheimer’s disease (AD)brain sub regionsdeep learning (DL)metaheuristic optimization techniquesMini-Mental State Examination (MMSE) score |
| spellingShingle | Chitradevi Dhakhinamoorthy Sathish Kumar Mani Sandeep Kumar Mathivanan Senthilkumar Mohan Prabhu Jayagopal Saurav Mallik Hong Qin Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease Mathematics Alzheimer’s disease (AD) brain sub regions deep learning (DL) metaheuristic optimization techniques Mini-Mental State Examination (MMSE) score |
| title | Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease |
| title_full | Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease |
| title_fullStr | Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease |
| title_full_unstemmed | Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease |
| title_short | Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease |
| title_sort | hybrid whale and gray wolf deep learning optimization algorithm for prediction of alzheimer s disease |
| topic | Alzheimer’s disease (AD) brain sub regions deep learning (DL) metaheuristic optimization techniques Mini-Mental State Examination (MMSE) score |
| url | https://www.mdpi.com/2227-7390/11/5/1136 |
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