A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning
Glaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diag...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.939472/full |
| _version_ | 1811238305976549376 |
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| author | Sanli Yi Gang Zhang Chaoxu Qian YunQing Lu Hua Zhong Jianfeng He |
| author_facet | Sanli Yi Gang Zhang Chaoxu Qian YunQing Lu Hua Zhong Jianfeng He |
| author_sort | Sanli Yi |
| collection | DOAJ |
| description | Glaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diagnosis of glaucoma. In this architecture, a gray scale image of the visual field is first reconstructed with a higher resolution in the preprocessing stage, and more subtle feature information is provided for glaucoma diagnosis. We then use multimodal fusion technology to integrate fundus images and gray scale images of the visual field as the input of this architecture. Finally, the inherent limitation of convolutional neural networks (CNNs) is addressed by replacing the original classifier with the proposed classifier. Our architecture is trained and tested on the datasets provided by the First Affiliated Hospital of Kunming Medical University, and the results show that the proposed architecture achieves superior performance for glaucoma diagnosis. |
| first_indexed | 2024-04-12T12:39:27Z |
| format | Article |
| id | doaj.art-7216ced9b746416f8778885249e944c8 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-12T12:39:27Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7216ced9b746416f8778885249e944c82022-12-22T03:32:48ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-06-011610.3389/fnins.2022.939472939472A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep LearningSanli Yi0Gang Zhang1Chaoxu Qian2YunQing Lu3Hua Zhong4Jianfeng He5School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaFirst Affiliated Hospital of Kunming Medical University, Kunming, ChinaFirst Affiliated Hospital of Kunming Medical University, Kunming, ChinaFirst Affiliated Hospital of Kunming Medical University, Kunming, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaGlaucoma is an optic neuropathy that leads to characteristic visual field defects. However, there is no cure for glaucoma, so the diagnosis of its severity is essential for its prevention. In this paper, we propose a multimodal classification architecture based on deep learning for the severity diagnosis of glaucoma. In this architecture, a gray scale image of the visual field is first reconstructed with a higher resolution in the preprocessing stage, and more subtle feature information is provided for glaucoma diagnosis. We then use multimodal fusion technology to integrate fundus images and gray scale images of the visual field as the input of this architecture. Finally, the inherent limitation of convolutional neural networks (CNNs) is addressed by replacing the original classifier with the proposed classifier. Our architecture is trained and tested on the datasets provided by the First Affiliated Hospital of Kunming Medical University, and the results show that the proposed architecture achieves superior performance for glaucoma diagnosis.https://www.frontiersin.org/articles/10.3389/fnins.2022.939472/fullglaucomacomputer-aided diagnosismultimodal fusionclassificationmulti-layer perceptron |
| spellingShingle | Sanli Yi Gang Zhang Chaoxu Qian YunQing Lu Hua Zhong Jianfeng He A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning Frontiers in Neuroscience glaucoma computer-aided diagnosis multimodal fusion classification multi-layer perceptron |
| title | A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning |
| title_full | A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning |
| title_fullStr | A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning |
| title_full_unstemmed | A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning |
| title_short | A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning |
| title_sort | multimodal classification architecture for the severity diagnosis of glaucoma based on deep learning |
| topic | glaucoma computer-aided diagnosis multimodal fusion classification multi-layer perceptron |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.939472/full |
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