Optimizing deep learning with improved Harris Hawks optimization for Alzheimer’s disease detection
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| Publicado en: | The Artificial Intelligence Review vol. 58, no. 10 (Oct 2025), p. 301 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 045 | 2 | |b d20251001 |b d20251031 | |
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| 100 | 1 | |a Zhang, Qian |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8); Wenzhou University of Technology, School of Data Science and Artificial Intelligence, Wenzhou, China (GRID:grid.411963.8) (ISNI:0000 0005 1164 4044) | |
| 245 | 1 | |a Optimizing deep learning with improved Harris Hawks optimization for Alzheimer’s disease detection | |
| 260 | |b Springer Nature B.V. |c Oct 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a As the global population ages, Alzheimer’s disease (AD) poses a significant worldwide challenge as a leading cause of dementia, with a slow early progression that eventually leads to nerve cell death and currently lacks effective treatment. However, early diagnosis can slow its progression through pharmaceutical intervention, making accurate early diagnosis using computer-aided diagnosis (CAD) systems crucial. This study aims to enhance the accuracy of early AD diagnosis by developing an improved optimization approach for deep learning-based CAD systems. To achieve this, this paper proposes an improved Harris Hawks optimization algorithm (HHO), named CAHHO, which incorporates crisscross search and adaptive β-Hill climbing mechanisms, thereby enhancing population diversity and search space coverage during the exploration phase, while adaptively adjusting the step size during the exploitation phase to improve local search precision. Comparative experiments with classical algorithms, HHO variants, and advanced optimization methods validate the superiority of the proposed CAHHO. Specifically, this study employs the deep learning model residual network with 18 layers (ResNet18) as the base model for AD diagnosis and uses CAHHO to optimize key hyperparameters, including the number of channels and learning rate. Experiments on the AD neuroimaging initiative dataset demonstrate that the ResNet18-CAHHO model outperforms existing methods in classifying AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Specifically, it achieves accuracies of 0.93077, 0.80102, and 0.80513 in the diagnosis of AD versus NC, MCI versus NC, and AD versus MCI, respectively. Furthermore, Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations reveal critical brain regions associated with AD, providing valuable diagnostic support for clinicians and holding significant promise for early intervention. | |
| 653 | |a Accuracy | ||
| 653 | |a Machine learning | ||
| 653 | |a Alzheimer's disease | ||
| 653 | |a Deep learning | ||
| 653 | |a Computer vision | ||
| 653 | |a Brain research | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Dementia | ||
| 653 | |a Magnetic resonance imaging | ||
| 653 | |a Neural networks | ||
| 653 | |a Medical imaging | ||
| 653 | |a Optimization | ||
| 653 | |a Searching | ||
| 653 | |a Classification | ||
| 653 | |a Algorithms | ||
| 653 | |a Diagnosis | ||
| 653 | |a Computer aided design--CAD | ||
| 653 | |a Cell death | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Experiments | ||
| 653 | |a Exploitation | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Brain | ||
| 653 | |a Models | ||
| 653 | |a Necrosis | ||
| 653 | |a Mapping | ||
| 653 | |a Cognitive impairment | ||
| 653 | |a Intervention | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Learning | ||
| 653 | |a Multiculturalism & pluralism | ||
| 653 | |a Early intervention | ||
| 653 | |a Variants | ||
| 653 | |a Disease | ||
| 653 | |a Climbing | ||
| 700 | 1 | |a Sheng, Jinhua |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8) | |
| 700 | 1 | |a Zhang, Qiao |u Beijing Hospital, Beijing, China (GRID:grid.414350.7) (ISNI:0000 0004 0447 1045); National Center of Gerontology, Beijing, China (GRID:grid.414350.7); Chinese Academy of Medical Sciences, Institute of Geriatric Medicine, Beijing, China (GRID:grid.506261.6) (ISNI:0000 0001 0706 7839) | |
| 700 | 1 | |a Yang, Ze |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8) | |
| 700 | 1 | |a Xin, Yu |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8) | |
| 700 | 1 | |a Wang, Binbing |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8) | |
| 700 | 1 | |a Zhang, Rong |u Hangzhou Dianzi University, School of Computer Science and Technology, Hangzhou, China (GRID:grid.411963.8) (ISNI:0000 0000 9804 6672); Ministry of Industry and Information Technology of P. R. China, Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Hangzhou, China (GRID:grid.411963.8) | |
| 773 | 0 | |t The Artificial Intelligence Review |g vol. 58, no. 10 (Oct 2025), p. 301 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3227719284/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3227719284/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3227719284/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |