Researchers applying machine learning to detect cause of blindness

Professor Esam Abdel-Raheem and doctoral student Sudipta Modak co-authored a paper on detecting a

eading cause of vision impairment.

Electrical and computer engineering professor Esam Abdel-Raheem is contributing to a work that is paving the way for early detection of diabetic retinopathy, a leading cause of vision impairment and blindness.

Making an earlier diagnosis will help physicians treat the condition and stave off serious deterioration, says Dr. Abdel-Raheem.

“If this diabetic retinopathy is detected early, over 90 per cent of vision loss can be prevented,” he says. “The current methods of screening rely on manual examinations, which are time-consuming, subjective, and prone to error. Adopting the presented deep learning techniques will result in saving vision and cutting down on overall health-care demands.”

A paper published in IEEE Access, “Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods,” is co-authored with his doctoral student Sudipta Modak and colleagues from the University of Sharjah, where Abdel-Raheem served as a visiting scholar. This is the first journal article resulting from the collaboration.

Courtesy: https://www.uwindsor.ca/dailynews/2024-09-24/researchers-applying-machine-learning-detect-cause-blindness