Separating of glaucoma and non-glaucoma fundus images using EfficientNet-B0

Abstract

Glaucoma is an eye disease that causes vision loss. This disease progresses silently without symptoms. Therefore, it is a difficult disease to detect. If glaucoma is detected before it progresses to advanced stages, vision loss can be prevented. Computer-aided diagnosis systems are preferred to understand whether the fundus image contains glaucoma. These systems provide accurate classification of healthy and glaucoma images. In this article, a system to separate images of a fundus dataset as glaucoma or healthy is proposed. The EfficientNet B0 model, which is a deep learning model, is used in the proposed system. The input of this deep network model is designed as six layers. The experimental results of the designed model were obtained on the publicly available ACRIMA dataset images. In the end, the average accuracy rate is determined as 0.9775.