ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2023
Development of a robust eye exam diagnosis platform with a deep learning model.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.Quantitative imaging in medicine and surgery · 2024Article
- Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
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Authors and funding
2 authors.
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Abstract
backgroundEye exam diagnosis is one of the early detection methods for eye diseases. However, such a method is dependent on expensive and unpredictable optical equipment.
objectiveThe 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.
methodsThe 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.
resultsA proposed model showed high training and validation accuracy values of 99.1% and 96.9%, respectively.
conclusionThe designed model could produce a robust and stable eye disease discrimination performance.
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