InceptionResNetV2 Based Eye Cataract Detection

Abstract

An eye lens is a key source for visualizing things. Sometimes it may be cloudy, caused by cataracts, and disturb the visualization process. It is necessary to detect and treat the cataract in its early stages; otherwise, it may lead to blindness. For the early detection of eye cataracts, many solutions have been proposed in past years by experts. However, it requires an expert ophthalmologist or eye surgeon to detect cataracts in its early stages because early detection of cataracts is a challenging task. Now this is the era of digitalization and AI. Everyone is talking about AI. Many researchers use AI to propose a solution to this problem. This study also proposed an AI-based cataract detection solution. A binary class eye fundus images data set was used for this research work. The ”Cataract” data set is a famous dataset for detecting cataracts on KAGGLE. Some image augmentation techniques were used to enhance the data set proficiency. In this study, InceptionResNetV2 was applied with some hyper-parameter tuning. The proposed methodology achieved a training accuracy of 99% and a validation accuracy of 98%.