Retinal Healthcare Diagnosis Approaches with Deep Learning Techniques

Abstract

The retina is an important organ of the human body, with a crucial function in the vision mechanism. A minor disturbance in the retina can cause various abnormalities in the eye, as well as complex retinal diseases such as diabetic retinopathy. To diagnose such diseases in early stages, many researchers are incorporating machine learning (ML) technique. The combination of medical science with ML improves the healthcare diagnosis systems of hospitals, clinics, and other providers. Recently, AI-based healthcare diagnosis systems assist clinicians in handling more patients in less time and improves diagnosis accuracy. In this paper, we review cutting-edge AI-based retinal diagnosis technologies. This article also briefly describes the potential of the latest densely connected convolutional networks (DenseNets) to improve the performance of diagnosis systems. Moreover, this paper focuses on state-of-the-art results from comprehensive investigations in retinal diagnosis and the development of AI-based retinal healthcare diagnosis approaches with deep-learning models.