Classification of Eye Disease from Retinal Images Using Deep Learning

Today, there are challenges in terms of eye care, including the treatment and prevention of visual impairment and vision rehabilitation services. Since the eye is an organ that gives information about other diseases due to its structure, examinations have an important role. This study, it is aimed to classify eye diseases with the EfficientNetB0, VGG-16, and VGG-19 models, which are deep learning-based approaches, over the dataset consisting of four classes of visual impairment from retinal images. In this way, it is aimed to reduce the visual impairment of patients with early diagnosis by identifying the people with the possibility of visual impairment, and determining which class they belong to. In this way, significant increases in the patient's quality of life can be observed. The results showed that, the best accuracy rate of 98.47% was obtained with the EfficientNetB0 model.