A Comprehensive Review on Detecting Diabetic Eye Diseases Using Deep Learning and Machine Learning Models

Abstract

Abstract: Diabetic Ocular Diseases (DOD), or Diabetic Eye Diseases (DED), is a set of eye problems that can affect people with all types of diabetes. Severe diabetes without proper diagnosis and control may lead to vision loss. It might damage the optical nerve, which causes poor/blurry vision or blindness. The problem includes Diabetic Retinopathy, Diabetic Macular Edema (DME), Glaucoma, and Cataracts. Diabetes people may frequently have this ailment, manifesting as fuzzy vision, floaters or streaks resembling cobwebs, retinal edema, impaired color perception, and eventually blindness. The primary issue is that these conditions are irreversible. So, timely diagnosis and treatments are a must. Many technologies, particularly Deep Learning (DL) and Machine Learning (ML), have evolved to predict and detect diseases earlier. This study aims to review some previously developed frameworks proposed by authors. Finding a reliable way to detect diseases early is the central stimulation behind this review. Most earlier works focused on categorizing images according to the severity of conditions. However, this review of the classification of diabetic eye diseases may give a proper path to identify suitable CNN for further work.