ACCV: automatic classification algorithm of cataract video based on deep learning

Shenming Hu, Xinze Luan, Hong Wu, Xiaoting Wang, Chunhong Yan, Jingying Wang, Guantong Liu, Wei He*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
27 Downloads (Pure)

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.

Original languageEnglish
Article number78
Number of pages17
JournalBioMedical Engineering Online
Volume20
DOIs
Publication statusPublished - 05 Aug 2021
Externally publishedYes

Keywords

  • Automatic cataract grading
  • Deep learning
  • YOLOv3

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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