Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism
Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone netw...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1941.pdf |
| _version_ | 1797234179029598208 |
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| author | Yuanyuan Chen Yongpeng Bai Yifan Zhang |
| author_facet | Yuanyuan Chen Yongpeng Bai Yifan Zhang |
| author_sort | Yuanyuan Chen |
| collection | DOAJ |
| description | Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model’s ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity. |
| first_indexed | 2024-04-24T16:27:57Z |
| format | Article |
| id | doaj.art-3bfb241b807b474c9be7bbfa87d4a3d4 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T16:27:57Z |
| publishDate | 2024-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-3bfb241b807b474c9be7bbfa87d4a3d42024-03-30T15:05:09ZengPeerJ Inc.PeerJ Computer Science2376-59922024-03-0110e194110.7717/peerj-cs.1941Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanismYuanyuan ChenYongpeng BaiYifan ZhangGlaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model’s ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.https://peerj.com/articles/cs-1941.pdfGlaucomaU-NetResidual moduleAttention mechanismImage segmentation |
| spellingShingle | Yuanyuan Chen Yongpeng Bai Yifan Zhang Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism PeerJ Computer Science Glaucoma U-Net Residual module Attention mechanism Image segmentation |
| title | Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism |
| title_full | Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism |
| title_fullStr | Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism |
| title_full_unstemmed | Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism |
| title_short | Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism |
| title_sort | optic disc and cup segmentation for glaucoma detection using attention u net incorporating residual mechanism |
| topic | Glaucoma U-Net Residual module Attention mechanism Image segmentation |
| url | https://peerj.com/articles/cs-1941.pdf |
| work_keys_str_mv | AT yuanyuanchen opticdiscandcupsegmentationforglaucomadetectionusingattentionunetincorporatingresidualmechanism AT yongpengbai opticdiscandcupsegmentationforglaucomadetectionusingattentionunetincorporatingresidualmechanism AT yifanzhang opticdiscandcupsegmentationforglaucomadetectionusingattentionunetincorporatingresidualmechanism |