FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention
Diabetic retinopathy (DR) is a major cause of vision loss. Accurate grading of DR is critical to ensure timely and appropriate intervention. DR progression is primarily characterized by the presence of biomarkers including microaneurysms, hemorrhages, and exudates. These markers are small, scattered...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-02-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2025033 |
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| author | Chang Yu Qian Ma Jing Li Qiuyang Zhang Jin Yao Biao Yan Zhenhua Wang |
| author_facet | Chang Yu Qian Ma Jing Li Qiuyang Zhang Jin Yao Biao Yan Zhenhua Wang |
| author_sort | Chang Yu |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a major cause of vision loss. Accurate grading of DR is critical to ensure timely and appropriate intervention. DR progression is primarily characterized by the presence of biomarkers including microaneurysms, hemorrhages, and exudates. These markers are small, scattered, and challenging to detect. To improve DR grading accuracy, we propose FF-ResNet-DR, a deep learning model that leverages frequency domain attention. Traditional attention mechanisms excel at capturing spatial-domain features but neglect valuable frequency domain information. Our model incorporates frequency channel attention modules (FCAM) and frequency spatial attention modules (FSAM). FCAM refines feature representation by fusing frequency and channel information. FSAM enhances the model's sensitivity to fine-grained texture details. Extensive experiments on multiple public datasets demonstrate the superior performance of FF-ResNet-DR compared to state-of-the-art models. It achieves an AUC of 98.1% on the Messidor binary classification task and a joint accuracy of 64.1% on the IDRiD grading task. These results highlight the potential of FF-ResNet-DR as a valuable tool for the clinical diagnosis and management of DR. |
| format | Article |
| id | doaj-art-bcdea22e9d72417e89dc16a30eb580b8 |
| institution | OA Journals |
| issn | 2688-1594 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-bcdea22e9d72417e89dc16a30eb580b82025-08-20T01:54:41ZengAIMS PressElectronic Research Archive2688-15942025-02-0133272574310.3934/era.2025033FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attentionChang Yu0Qian Ma1Jing Li2Qiuyang Zhang3Jin Yao4Biao Yan5Zhenhua Wang6College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaGeneral Hospital of Ningxia Medical University, Ningxia 750001, ChinaEye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai 201114, ChinaDepartment of Ophthalmology and Optometry, The Affiliated Eye Hospital, Nanjing Medical University, Nanjing 210029, ChinaDepartment of Ophthalmology and Optometry, The Affiliated Eye Hospital, Nanjing Medical University, Nanjing 210029, ChinaDepartment of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaDiabetic retinopathy (DR) is a major cause of vision loss. Accurate grading of DR is critical to ensure timely and appropriate intervention. DR progression is primarily characterized by the presence of biomarkers including microaneurysms, hemorrhages, and exudates. These markers are small, scattered, and challenging to detect. To improve DR grading accuracy, we propose FF-ResNet-DR, a deep learning model that leverages frequency domain attention. Traditional attention mechanisms excel at capturing spatial-domain features but neglect valuable frequency domain information. Our model incorporates frequency channel attention modules (FCAM) and frequency spatial attention modules (FSAM). FCAM refines feature representation by fusing frequency and channel information. FSAM enhances the model's sensitivity to fine-grained texture details. Extensive experiments on multiple public datasets demonstrate the superior performance of FF-ResNet-DR compared to state-of-the-art models. It achieves an AUC of 98.1% on the Messidor binary classification task and a joint accuracy of 64.1% on the IDRiD grading task. These results highlight the potential of FF-ResNet-DR as a valuable tool for the clinical diagnosis and management of DR.https://www.aimspress.com/article/doi/10.3934/era.2025033diabetic retinopathy (dr)grading modelclassification modelcolor fundus imageattention mechanism |
| spellingShingle | Chang Yu Qian Ma Jing Li Qiuyang Zhang Jin Yao Biao Yan Zhenhua Wang FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention Electronic Research Archive diabetic retinopathy (dr) grading model classification model color fundus image attention mechanism |
| title | FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention |
| title_full | FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention |
| title_fullStr | FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention |
| title_full_unstemmed | FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention |
| title_short | FF-ResNet-DR model: a deep learning model for diabetic retinopathy grading by frequency domain attention |
| title_sort | ff resnet dr model a deep learning model for diabetic retinopathy grading by frequency domain attention |
| topic | diabetic retinopathy (dr) grading model classification model color fundus image attention mechanism |
| url | https://www.aimspress.com/article/doi/10.3934/era.2025033 |
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