Detection of Diabetic Retinopathy Based on Convolutional Neural Networks: A Review
Halbast Rashid Ismael *
Akre Technical College of Informatics, Duhok Polytechnic University, Duhok, KurdistanRegion, Iraq.
Adnan Mohsin Abdulazeez
Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq.
Dathar Abas Hasan
Shekhan Technical Institute,Duhok Polytechnic University, Duhok, KurdistanRegion, Iraq.
*Author to whom correspondence should be addressed.
Abstract
A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense, and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.
Keywords: Diabetic retinopathy, retinal images, detection, convolutional neural networks