Title:Deep Learning Techniques for Diabetic Retinopathy Detection
VOLUME: 16 ISSUE: 10
Author(s):Sehrish Qummar, Fiaz Gul Khan*, Sajid Shah, Ahmad Khan, Ahmad Din and Jinfeng Gao
Affiliation:Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Department of Information Engineering, Huanghuai University, Henan
Keywords:Diabetic retinopathy, deep learning, convolutional Neural Network, diabetes, machine learning, lesions detection.
Abstract:Diabetes occurs due to the excess of glucose in the blood that may affect many organs
of the body. Elevated blood sugar in the body causes many problems including Diabetic Retinopathy
(DR). DR occurs due to the mutilation of the blood vessels in the retina. The manual detection
of DR by ophthalmologists is complicated and time-consuming. Therefore, automatic detection is
required, and recently different machine and deep learning techniques have been applied to detect
and classify DR. In this paper, we conducted a study of the various techniques available in the literature
for the identification/classification of DR, the strengths and weaknesses of available datasets
for each method, and provides the future directions. Moreover, we also discussed the different
steps of detection, that are: segmentation of blood vessels in a retina, detection of lesions, and other
abnormalities of DR.
