ABSTRACT
Diabetes is a condition in which a person’s body either does not respond to insulin supplied by their pancreas or does not create enough insulin. Diabetics are at a higher chance and risk of acquiring a variety of eye disorders over time. Early identification of eye diseases via an automated method has significant advantages over manual detection thanks to developments in machine learning techniques. Recently, some high research articles on the identification of eye diseases have been published. This paper will present a comprehensive survey of automated eye diseases detection systems which are Strabismus, Glaucoma, and Diabetic Retinopathy from a variety of perspectives, including (1) datasets that are available, (2) techniques of image preprocessing, and (3) deep learning models. The study offers a thorough overview of eye disease detection methods, including cutting-edge field methods, intending to provide vital insight into the research communities, all eye-related healthcare occupational, and diabetic patients.
Keywords: Strabismus, Glaucoma, Diabetic retinopathy, Convolutional neural network, and Deep learning.
Citation: Kamal MM, Shanto MHI, Hossan MM, Hasnat MA, Sultana S, and Biswas M. (2022). A comprehensive review on the diabetic retinopathy, glaucoma, and strabismus detection techniques based on machine learning and deep learning. Eur. J. Med. Health Sci., 4(2), 24-40. https://doi.org/10.34104/ejmhs.022.024040
