Review of Machine Learning Applications Using Retinal Fundus Images

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

Automated summary

Automating screening and diagnosis in the medical field saves time, reduces chances of misdiagnosis, and saves on labor and costs. With the development of deep learning methods, machines are now capable of interpreting complex features in medical data, leading to rapid advancements in automation. This paper reviews recent state-of-the-art works in ophthalmology that utilize color fundus images for automated screening and diagnosis of diabetic retinopathy, age-related macular degeneration, and glaucoma. The challenges in developing these systems are also discussed.