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

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.