ACCV: automatic classification algorithm of cataract video based on deep learning.
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 th...
| Publicado en: | BioMedical Engineering OnLine Vol. 20; no. 1; pp. 1 - 18 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/5/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151774052&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151774052 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 8/5/2021 vid: 20 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 151774052 151774052 NLM34353324 10.1186/s12938-021-00906-3 NLM34353324 151774052 ppf: 1 ppct: 17 formats: tig: atl: ACCV: automatic classification algorithm of cataract video based on deep learning. aug: au: Hu, Shenming Luan, Xinze Wu, Hong Wang, Xiaoting Yan, Chunhong Wang, Jingying Liu, Guantong He, Wei affil: College of Medicine and Biological Information Engineering, Northeastern University, 110016, Shenyang, China sug: subj: Cataract Diagnosis Lens, Crystalline Algorithms Retrospective Design Family Inventory of Life Events and Changes Scales Arthritis Impact Measurement Scales ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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