Diabetic Retinopathy Tracker System

The examination of retinal images is compulsory for ophthalmologists to spot features of eye diseases. Multiple
studies and clinical trials have reported the benefit of early detection and timely treatment in reducing
the risk of vision loss from Diabetic Retinopathy (DR) and decreasing the global burden of blindness. Active
screening for DR is important because most patients who develop DR have no symptoms until the very late
stages, and by then it is often too late for effective treatment. Multiple patient barriers to DR screening exist,
including poor access to care rural healthy deprivation, lack of time, high out-of-pocket expenses, insufficient
patient knowledge and awareness of DR, and lack of care coordination, especially among low-income populations,
and ethnical minorities specifically in Malaysia. Hence, this project aims at developing an automated
intelligence system based on fundus image captured by using a Convolutional Neural Network (CNN) trained
by a Deep Learning (DL) algorithm. The system is named Diabetic Retinopathy Tracker system (DR Tracker) assists medical officers/technologists in rural areas in the screening process based on raw fundus images captured.
In this project, 200 images were used for testing, while 2000 images were used for training. The results achieved optimum accuracy is 93.75%. This product innovation known as DR Tracker will contribute to an
early screening process by improving the performance accuracy for the detection of DR based on the fundus image. By utilizing classification algorithms, such solutions have the potential to provide quick, accurate decision
support at a low cost. It can benefit the patients in medically underserved areas that have limited numbers
of ophthalmologists and rare medical resources.