ACCV: automatic classification algorithm of cataract video based on deep learning
Abstract Purpose A real-time automatic cataract-grading algorithm based on cataract video is proposed. Materials and methods In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically ide...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-08-01
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| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-021-00906-3 |
| _version_ | 1819086194371198976 |
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| author | Shenming Hu Xinze Luan Hong Wu Xiaoting Wang Chunhong Yan Jingying Wang Guantong Liu Wei He |
| author_facet | Shenming Hu Xinze Luan Hong Wu Xiaoting Wang Chunhong Yan Jingying Wang Guantong Liu Wei He |
| author_sort | Shenming Hu |
| collection | DOAJ |
| description | Abstract Purpose A real-time automatic cataract-grading algorithm based on cataract video is proposed. Materials and methods In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically identify the position of the lens and classify the cataract after color space conversion. The data set is a cataract video file of 38 people's 76 eyes collected by a slit lamp. Data were collected using five random manner, the method aims to reduce the influence on the collection algorithm accuracy. The video length is within 10 s, and the classified picture data are extracted from the video file. A total of 1520 images are extracted from the image data set, and the data set is divided into training set, validation set and test set according to the ratio of 7:2:1. Results We verified it on the 76-segment clinical data test set and achieved the accuracy of 0.9400, with the AUC of 0.9880, and the F1 of 0.9388. In addition, because of the color space recognition method, the detection per frame can be completed within 29 microseconds and thus the detection efficiency has been improved significantly. Conclusion With the efficiency and effectiveness of this algorithm, the lens scan video is used as the research object, which improves the accuracy of the screening. It is closer to the actual cataract diagnosis and treatment process, and can effectively improve the cataract inspection ability of non-ophthalmologists. For cataract screening in poor areas, the accessibility of ophthalmology medical care is also increased. |
| first_indexed | 2024-12-21T21:16:22Z |
| format | Article |
| id | doaj.art-cc51c91545b4496182c764b6c61a9714 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-12-21T21:16:22Z |
| publishDate | 2021-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-cc51c91545b4496182c764b6c61a97142022-12-21T18:49:59ZengBMCBioMedical Engineering OnLine1475-925X2021-08-0120111710.1186/s12938-021-00906-3ACCV: automatic classification algorithm of cataract video based on deep learningShenming Hu0Xinze Luan1Hong Wu2Xiaoting Wang3Chunhong Yan4Jingying Wang5Guantong Liu6Wei He7College of Medicine and Biological Information Engineering, Northeastern UniversityHe UniversityShenyang Eyerobo Co., Ltd.He UniversityHe Eye Specialists HospitalShenyang Eyerobo Co., Ltd.He Eye Specialists HospitalHe Eye Specialists HospitalAbstract Purpose A real-time automatic cataract-grading algorithm based on cataract video is proposed. Materials and methods In this retrospective study, we set the video of the eye lens section as the research target. A method is proposed to use YOLOv3 to assist in positioning, to automatically identify the position of the lens and classify the cataract after color space conversion. The data set is a cataract video file of 38 people's 76 eyes collected by a slit lamp. Data were collected using five random manner, the method aims to reduce the influence on the collection algorithm accuracy. The video length is within 10 s, and the classified picture data are extracted from the video file. A total of 1520 images are extracted from the image data set, and the data set is divided into training set, validation set and test set according to the ratio of 7:2:1. Results We verified it on the 76-segment clinical data test set and achieved the accuracy of 0.9400, with the AUC of 0.9880, and the F1 of 0.9388. In addition, because of the color space recognition method, the detection per frame can be completed within 29 microseconds and thus the detection efficiency has been improved significantly. Conclusion With the efficiency and effectiveness of this algorithm, the lens scan video is used as the research object, which improves the accuracy of the screening. It is closer to the actual cataract diagnosis and treatment process, and can effectively improve the cataract inspection ability of non-ophthalmologists. For cataract screening in poor areas, the accessibility of ophthalmology medical care is also increased.https://doi.org/10.1186/s12938-021-00906-3Automatic cataract gradingDeep learningYOLOv3 |
| spellingShingle | Shenming Hu Xinze Luan Hong Wu Xiaoting Wang Chunhong Yan Jingying Wang Guantong Liu Wei He ACCV: automatic classification algorithm of cataract video based on deep learning BioMedical Engineering OnLine Automatic cataract grading Deep learning YOLOv3 |
| title | ACCV: automatic classification algorithm of cataract video based on deep learning |
| title_full | ACCV: automatic classification algorithm of cataract video based on deep learning |
| title_fullStr | ACCV: automatic classification algorithm of cataract video based on deep learning |
| title_full_unstemmed | ACCV: automatic classification algorithm of cataract video based on deep learning |
| title_short | ACCV: automatic classification algorithm of cataract video based on deep learning |
| title_sort | accv automatic classification algorithm of cataract video based on deep learning |
| topic | Automatic cataract grading Deep learning YOLOv3 |
| url | https://doi.org/10.1186/s12938-021-00906-3 |
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