Anomaly Detection in Red Reflex Images Using Deep Learning Approaches
Issued Date
2022-01-01
Resource Type
ISSN
21593442
eISSN
21593450
Scopus ID
2-s2.0-85145649664
Journal Title
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume
2022-November
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON Vol.2022-November (2022)
Suggested Citation
Kriangsakdachai S., Ayudhya S.P.N., Kusakunniran W., Ayudhya W.D.N., Chantrasagul C., Manasboonpermpool R., Sathianvichitr K., Sangsre P., Surachatkumtonekul T. Anomaly Detection in Red Reflex Images Using Deep Learning Approaches. IEEE Region 10 Annual International Conference, Proceedings/TENCON Vol.2022-November (2022). doi:10.1109/TENCON55691.2022.9977626 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84324
Title
Anomaly Detection in Red Reflex Images Using Deep Learning Approaches
Author's Affiliation
Other Contributor(s)
Abstract
Amblyopia is a noteworthy disease in children leading to visual loss. This work focuses on creating a deep learning model for the detection of Amblyopia factors in patients wearing masks under the COVID-19 pandemic.