A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level
Alzheimer’s disease is a neurodegenerative disorder prevalent in older adults, and early diagnosis is crucial for effective treatment. A deep learning model can automatically classify Alzheimer’s disease from magnetic resonance imaging to aid clinicians in diagnosis. Convolutio...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10483069/ |
| _version_ | 1797227293164175360 |
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| author | Mahir Kaya Yasemin Cetin-Kaya |
| author_facet | Mahir Kaya Yasemin Cetin-Kaya |
| author_sort | Mahir Kaya |
| collection | DOAJ |
| description | Alzheimer’s disease is a neurodegenerative disorder prevalent in older adults, and early diagnosis is crucial for effective treatment. A deep learning model can automatically classify Alzheimer’s disease from magnetic resonance imaging to aid clinicians in diagnosis. Convolutional Neural Networks (CNNs) are commonly used for disease detection in medical images, but their performance is limited due to inadequate labeled data, high inter-class similarity, and overfitting problems. Key hyperparameters influencing CNN performance include the number of convolution layers and filters assigned to each convolution layer. About other hyperparameters, numerous combinations exist. Since CNN models take a long time to train, it is quite costly to try all combinations to find the optimal model. Existing studies have optimized only a few hyperparameters, such as learning rate, batch size, and optimizer in custom and transfer learning models. In this study, we propose an algorithm based on particle swarm optimization to fine-tune the hyperparameters, including the number of convolution layers, filters, and other hyperparameters, in CNN architectures designed to classify Alzheimer’s disease severity. Using the proposed lightweight model, Alzheimer’s disease was accurately classified with an accuracy of 99.53% and an F1-score of 99.63% on a public dataset. Our model surpasses the performance of previous studies, offering the potential to alleviate the burden on doctors and expedite their decision-making processes. The developed framework can be accessed via the link: “<uri>https://ai.gop.edu.tr/alzheimer</uri>”. |
| first_indexed | 2024-04-24T14:38:30Z |
| format | Article |
| id | doaj.art-60c70469777246db8fd58c14d7a7b683 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T14:38:30Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-60c70469777246db8fd58c14d7a7b6832024-04-02T23:00:23ZengIEEEIEEE Access2169-35362024-01-0112465624658110.1109/ACCESS.2024.338294710483069A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease LevelMahir Kaya0https://orcid.org/0000-0001-9182-271XYasemin Cetin-Kaya1https://orcid.org/0000-0002-6745-7705Department of Computer Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpaşa University, Tokat, TurkeyDepartment of Computer Engineering, Faculty of Engineering and Architecture, Tokat Gaziosmanpaşa University, Tokat, TurkeyAlzheimer’s disease is a neurodegenerative disorder prevalent in older adults, and early diagnosis is crucial for effective treatment. A deep learning model can automatically classify Alzheimer’s disease from magnetic resonance imaging to aid clinicians in diagnosis. Convolutional Neural Networks (CNNs) are commonly used for disease detection in medical images, but their performance is limited due to inadequate labeled data, high inter-class similarity, and overfitting problems. Key hyperparameters influencing CNN performance include the number of convolution layers and filters assigned to each convolution layer. About other hyperparameters, numerous combinations exist. Since CNN models take a long time to train, it is quite costly to try all combinations to find the optimal model. Existing studies have optimized only a few hyperparameters, such as learning rate, batch size, and optimizer in custom and transfer learning models. In this study, we propose an algorithm based on particle swarm optimization to fine-tune the hyperparameters, including the number of convolution layers, filters, and other hyperparameters, in CNN architectures designed to classify Alzheimer’s disease severity. Using the proposed lightweight model, Alzheimer’s disease was accurately classified with an accuracy of 99.53% and an F1-score of 99.63% on a public dataset. Our model surpasses the performance of previous studies, offering the potential to alleviate the burden on doctors and expedite their decision-making processes. The developed framework can be accessed via the link: “<uri>https://ai.gop.edu.tr/alzheimer</uri>”.https://ieeexplore.ieee.org/document/10483069/Deep learningconvolutional neural networkAlzheimeroptimizationhyperparameter |
| spellingShingle | Mahir Kaya Yasemin Cetin-Kaya A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level IEEE Access Deep learning convolutional neural network Alzheimer optimization hyperparameter |
| title | A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level |
| title_full | A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level |
| title_fullStr | A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level |
| title_full_unstemmed | A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level |
| title_short | A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level |
| title_sort | novel deep learning architecture optimization for multiclass classification of alzheimer x2019 s disease level |
| topic | Deep learning convolutional neural network Alzheimer optimization hyperparameter |
| url | https://ieeexplore.ieee.org/document/10483069/ |
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