Development of a robust eye exam diagnosis platform with a deep learning model
- Authors
- Heo, Sung-Phil; Choi, Hojong
- Issue Date
- Apr-2023
- Publisher
- IOS Press
- Keywords
- Deep learning model; eye exam; image data
- Citation
- Technology and Health Care, v.31, no.S1, pp.S423 - S428
- Journal Title
- Technology and Health Care
- Volume
- 31
- Number
- S1
- Start Page
- S423
- End Page
- S428
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/88082
- DOI
- 10.3233/thc-236036
- ISSN
- 0928-7329
- Abstract
- BACKGROUND: Eye exam diagnosis is one of the early detection methods for eye diseases. However, such a method is dependent on expensive and unpredictable optical equipment.
OBJECTIVE: The eye exam can be re-emerged through an optometric lens attached to a smartphone and come to read the diseases automatically. Therefore, this study aims to provide a stable and predictable model with a given dataset representing the target group domain and develop a new method to identify eye disease with accurate and stable performance.
METHODS: The ResNet-18 models pre-trained on ImageNet data composed of 1,000 everyday objects were employed to learn the dataset’s features and validate the test dataset separated from the training dataset.
RESULTS: A proposed model showed high training and validation accuracy values of 99.1% and 96.9%, respectively.
CONCLUSION: The designed model could produce a robust and stable eye disease discrimination performance.
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