Convolutional Neural Network Modeling for Eye Disease Recognition

Abstract

The eye is an important sensing organ of the human body, as it reacts to light and allows vision for humans. Many Bangladeshi people become nearsighted when it comes to the awareness of vision loss due to eye disease. Many Bangladeshis people are more concerned about losing their money than getting nearsighted or blind, due to a combination of poverty and illiteracy. With this view, this paper proposes an osteopathic expert system that can deal with an image of the eye and recognize the disease. Here, we have focused on the three most common eye diseases in Bangladesh, namely cataract, chalazion, and squint. We have modeled six convolutional neural networks (CNN’s), namely VGG16, VGG19, MobileNet, Xception, InceptionV3, and DenseNet121 to recognize the diseases. We have reached the best configuration of each of these CNN models after adequate investigation. After performing satisfactory experimentation, we have found that the MobileNet model gives the best performance based on accuracy, precision, recall, and F1-score. At last, we have compared our findings with the recently reported relevant works to show their efficacy.