Study of Minor Dry-Eye Detection Using Smartphone Camera Based on Deep Learning

A method for minor dry-eye detection based on deep learning was developed. Images of eyes were captured using a smartphone digital camera, and tear volume tests were performed using phenol red thread, to generate datasets of eye images and tear volumes used in our experiments. Our simple neural network models and ResNet 50 were applied to 2-class classification and evaluated. According to the results, it was difficult for humans to distinguish minor dry-eyes from our datasets, whereas the accuracy of ResNet 50 exceeded 0.80 and 0.75 on our validation and test datasets, respectively. Because there are few differences between the eye images in the dataset, the gradient in the deep layer was lost, and learning did not proceed to the deeper layers. However, when skip connection was applied, dry-eye detection via ResNet 50 became possible and resulted in the maximum accuracy among all tested methods. After the training, it was possible to detect minor dry-eyes using only a smartphone.