Alzheimer's disease image classification based on enhanced residual attention network.
With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317376 |
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| _version_ | 1832540210063212544 |
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| author | Xiaoli Li Bairui Gong Xinfang Chen Hui Li Guoming Yuan |
| author_facet | Xiaoli Li Bairui Gong Xinfang Chen Hui Li Guoming Yuan |
| author_sort | Xiaoli Li |
| collection | DOAJ |
| description | With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates. To address these issues, this study proposes a deep learning model to detect Alzheimer's disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. The accuracy of the model in detecting Alzheimer's disease has reached 99.36%, with a loss rate of only 0.0264. The experimental results indicate that the Enhanced Residual Attention Network has achieved excellent performance on the Alzheimer's disease test dataset, providing strong support for the early diagnosis and treatment of Alzheimer's disease. |
| format | Article |
| id | doaj-art-c9281f2d41fc443495018cba6f753388 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c9281f2d41fc443495018cba6f7533882025-02-05T05:32:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031737610.1371/journal.pone.0317376Alzheimer's disease image classification based on enhanced residual attention network.Xiaoli LiBairui GongXinfang ChenHui LiGuoming YuanWith the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates. To address these issues, this study proposes a deep learning model to detect Alzheimer's disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. The accuracy of the model in detecting Alzheimer's disease has reached 99.36%, with a loss rate of only 0.0264. The experimental results indicate that the Enhanced Residual Attention Network has achieved excellent performance on the Alzheimer's disease test dataset, providing strong support for the early diagnosis and treatment of Alzheimer's disease.https://doi.org/10.1371/journal.pone.0317376 |
| spellingShingle | Xiaoli Li Bairui Gong Xinfang Chen Hui Li Guoming Yuan Alzheimer's disease image classification based on enhanced residual attention network. PLoS ONE |
| title | Alzheimer's disease image classification based on enhanced residual attention network. |
| title_full | Alzheimer's disease image classification based on enhanced residual attention network. |
| title_fullStr | Alzheimer's disease image classification based on enhanced residual attention network. |
| title_full_unstemmed | Alzheimer's disease image classification based on enhanced residual attention network. |
| title_short | Alzheimer's disease image classification based on enhanced residual attention network. |
| title_sort | alzheimer s disease image classification based on enhanced residual attention network |
| url | https://doi.org/10.1371/journal.pone.0317376 |
| work_keys_str_mv | AT xiaolili alzheimersdiseaseimageclassificationbasedonenhancedresidualattentionnetwork AT bairuigong alzheimersdiseaseimageclassificationbasedonenhancedresidualattentionnetwork AT xinfangchen alzheimersdiseaseimageclassificationbasedonenhancedresidualattentionnetwork AT huili alzheimersdiseaseimageclassificationbasedonenhancedresidualattentionnetwork AT guomingyuan alzheimersdiseaseimageclassificationbasedonenhancedresidualattentionnetwork |