A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique
A progressive brain disease that affects memory and cognitive function is Alzheimer’s disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential. Early detection of these phases enables early activities, which are essential for co...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1558725/full |
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| author | A. S. Elmotelb Fayroz F. Sherif A. S. Abohamama A. S. Abohamama Mahmoud Fakhr Amr M. Abdelatif |
| author_facet | A. S. Elmotelb Fayroz F. Sherif A. S. Abohamama A. S. Abohamama Mahmoud Fakhr Amr M. Abdelatif |
| author_sort | A. S. Elmotelb |
| collection | DOAJ |
| description | A progressive brain disease that affects memory and cognitive function is Alzheimer’s disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential. Early detection of these phases enables early activities, which are essential for controlling the disease. To address issues with limited data and computing resources, this work presents a novel deep-learning method based on using a newly proposed hyperparameter optimization method to identify the hyperparameters of ResNet152V2 model for classifying the phases of AD more accurately. The proposed model is compared to state-of-the-art models divided into two categories: transfer learning models and classical models to showcase its effectiveness and efficiency. This comparison is based on four performance metrics: recall, precision, F1 score, and accuracy. According to the experimental results, the proposed method is more efficient and effective in classifying various AD phases. |
| format | Article |
| id | doaj-art-79dfcabe0f464af0a611703e65f19d2e |
| institution | OA Journals |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-79dfcabe0f464af0a611703e65f19d2e2025-08-20T02:12:37ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-04-01810.3389/frai.2025.15587251558725A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization techniqueA. S. Elmotelb0Fayroz F. Sherif1A. S. Abohamama2A. S. Abohamama3Mahmoud Fakhr4Amr M. Abdelatif5Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, EgyptComputers and Systems Department, Electronics Research Institute (ERI), Cairo, EgyptDepartment of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, EgyptDepartment of Computer Science, Arab East Colleges, Riyadh, Saudi ArabiaComputers and Systems Department, Electronics Research Institute (ERI), Cairo, EgyptDepartment of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig, EgyptA progressive brain disease that affects memory and cognitive function is Alzheimer’s disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential. Early detection of these phases enables early activities, which are essential for controlling the disease. To address issues with limited data and computing resources, this work presents a novel deep-learning method based on using a newly proposed hyperparameter optimization method to identify the hyperparameters of ResNet152V2 model for classifying the phases of AD more accurately. The proposed model is compared to state-of-the-art models divided into two categories: transfer learning models and classical models to showcase its effectiveness and efficiency. This comparison is based on four performance metrics: recall, precision, F1 score, and accuracy. According to the experimental results, the proposed method is more efficient and effective in classifying various AD phases.https://www.frontiersin.org/articles/10.3389/frai.2025.1558725/fullAlzheimer’s disease phasesmulti-classificationdeep learninghyperparametersResNet152V25 |
| spellingShingle | A. S. Elmotelb Fayroz F. Sherif A. S. Abohamama A. S. Abohamama Mahmoud Fakhr Amr M. Abdelatif A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique Frontiers in Artificial Intelligence Alzheimer’s disease phases multi-classification deep learning hyperparameters ResNet152V25 |
| title | A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique |
| title_full | A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique |
| title_fullStr | A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique |
| title_full_unstemmed | A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique |
| title_short | A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique |
| title_sort | novel deep learning technique for multi classify alzheimer disease hyperparameter optimization technique |
| topic | Alzheimer’s disease phases multi-classification deep learning hyperparameters ResNet152V25 |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1558725/full |
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