Anterior Segment Eye Abnormality Detection

Vision is the most critical sense helping us to understand the world around us. Ophthalmology is an area of medicine that deals with the eye and vision. In many remote areas, people do not have access to ophthalmologists, and many go blind for preventable reasons. Awareness about eye health and early diagnosis is essential in eye health to prevent blindness. An artificial intelligence (AI) algorithm that can quickly detect eye disease is valuable and necessary. Anterior segment eye images are essential and easily obtained without additional equipment. In this study, I aimed to build an artificial intelligence algorithm to detect eye diseases from mobile photographs. I extracted and combined anterior segment eye photos from various publicly available datasets and labeled 3938 images as Normal (healthy) and 1094 images as Abnormal (unhealthy). I increased the data diversity by augmenting it with random flips and rotations: and then prepared it for AI training. I re-trained the algorithms trained in ImageNet Visual Recognition Challenge with the transfer learning method. I compared custom and pre-trained models. After evaluating the performance of the models with the test set, 98% accuracy and 97% F1 score were obtained with the Inception-ResNetV2 model.