Research on the automatic classification system of pterygium based on deep learning
AIM: To evaluate the application value of the automatic classification and diagnosis system of pterygium based on deep learning.METHODS: A total of 750 images of normal, observational and operative anterior sections of pterygium were collected from the Affiliated Eye Hospital of Nanjing Medical Univ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Press of International Journal of Ophthalmology (IJO PRESS)
2022-05-01
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| Series: | Guoji Yanke Zazhi |
| Subjects: | |
| Online Access: | http://ies.ijo.cn/cn_publish/2022/5/202205003.pdf |
| _version_ | 1818272066722332672 |
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| author | Kai He Mao-Nian Wu Bo Zheng Wei-Hua Yang Shao-Jun Zhu Ling Jin |
| author_facet | Kai He Mao-Nian Wu Bo Zheng Wei-Hua Yang Shao-Jun Zhu Ling Jin |
| author_sort | Kai He |
| collection | DOAJ |
| description | AIM: To evaluate the application value of the automatic classification and diagnosis system of pterygium based on deep learning.METHODS: A total of 750 images of normal, observational and operative anterior sections of pterygium were collected from the Affiliated Eye Hospital of Nanjing Medical University between May 2020 and April 2021. Seven triclassification models were respectively trained with original data set and enhanced data set. Totally 470 clinical images were tested, and the generalization ability of the model before and after data enhancement was compared to determine the best model for the automatic classification system of pterygium.RESULTS:The average sensitivity, specificity and AUC of the best model trained on the original data set were 92.55%, 96.86% and 94.70% respectively. After data was enhanced, the sensitivity, specificity and AUC of different models were increased by 3.7%, 1.9% and 2.7% on average. The sensitivity, specificity and AUC of the EfficientNetB7 model trained on the enhanced data set were 93.63%, 97.34% and 95.47% respectively.CONDLUSION: The EfficientNetB7 model, which was trained on the enhanced data set, achieves the best classification result and can be used in the automatic classification system of pterygium.This automatic classification system can diagnose diseases about pterygium better, and it is expected to be an effective screening tool for primary medical care. It also provides reference for the research on the refinement and grading of pterygium. |
| first_indexed | 2024-12-12T21:36:10Z |
| format | Article |
| id | doaj.art-eeac4f785a384e669bd887910c93b0dc |
| institution | Directory Open Access Journal |
| issn | 1672-5123 |
| language | English |
| last_indexed | 2024-12-12T21:36:10Z |
| publishDate | 2022-05-01 |
| publisher | Press of International Journal of Ophthalmology (IJO PRESS) |
| record_format | Article |
| series | Guoji Yanke Zazhi |
| spelling | doaj.art-eeac4f785a384e669bd887910c93b0dc2022-12-22T00:11:11ZengPress of International Journal of Ophthalmology (IJO PRESS)Guoji Yanke Zazhi1672-51232022-05-0122571171510.3980/j.issn.1672-5123.2022.5.03202205003Research on the automatic classification system of pterygium based on deep learningKai He0Mao-Nian Wu1Bo Zheng2Wei-Hua Yang3Shao-Jun Zhu4Ling Jin5School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, ChinaBig Data Laboratory of Ophthalmic Artificial Intelligence;the Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China; Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, Zhejiang Province, ChinaBig Data Laboratory of Ophthalmic Artificial Intelligence;the Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, ChinaAIM: To evaluate the application value of the automatic classification and diagnosis system of pterygium based on deep learning.METHODS: A total of 750 images of normal, observational and operative anterior sections of pterygium were collected from the Affiliated Eye Hospital of Nanjing Medical University between May 2020 and April 2021. Seven triclassification models were respectively trained with original data set and enhanced data set. Totally 470 clinical images were tested, and the generalization ability of the model before and after data enhancement was compared to determine the best model for the automatic classification system of pterygium.RESULTS:The average sensitivity, specificity and AUC of the best model trained on the original data set were 92.55%, 96.86% and 94.70% respectively. After data was enhanced, the sensitivity, specificity and AUC of different models were increased by 3.7%, 1.9% and 2.7% on average. The sensitivity, specificity and AUC of the EfficientNetB7 model trained on the enhanced data set were 93.63%, 97.34% and 95.47% respectively.CONDLUSION: The EfficientNetB7 model, which was trained on the enhanced data set, achieves the best classification result and can be used in the automatic classification system of pterygium.This automatic classification system can diagnose diseases about pterygium better, and it is expected to be an effective screening tool for primary medical care. It also provides reference for the research on the refinement and grading of pterygium.http://ies.ijo.cn/cn_publish/2022/5/202205003.pdfartificial intelligencedeep learningpterygiumclassification modeldata-enhancementtransfer learning |
| spellingShingle | Kai He Mao-Nian Wu Bo Zheng Wei-Hua Yang Shao-Jun Zhu Ling Jin Research on the automatic classification system of pterygium based on deep learning Guoji Yanke Zazhi artificial intelligence deep learning pterygium classification model data-enhancement transfer learning |
| title | Research on the automatic classification system of pterygium based on deep learning |
| title_full | Research on the automatic classification system of pterygium based on deep learning |
| title_fullStr | Research on the automatic classification system of pterygium based on deep learning |
| title_full_unstemmed | Research on the automatic classification system of pterygium based on deep learning |
| title_short | Research on the automatic classification system of pterygium based on deep learning |
| title_sort | research on the automatic classification system of pterygium based on deep learning |
| topic | artificial intelligence deep learning pterygium classification model data-enhancement transfer learning |
| url | http://ies.ijo.cn/cn_publish/2022/5/202205003.pdf |
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