Detection of Eye Disease Using Deep Learning Algorithms

The human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. This paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. The paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). The results from the conducted experiments show that ViT has the highest accuracy followed by CNN. The outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology.