Development of a robust eye exam diagnosis platform with a deep learning model.

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Abstract

Eye exam diagnosis is one of the early detection methods. However, such a method is dependent on expensive and unpredictable optical equipment.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.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.A proposed model showed high training and validation accuracy values of 99.1% and 96.9%, respectively.The designed model could produce a robust and stable eye disease discrimination performance.