Diabetic Retinopathy Segmentation in IDRiD using Enhanced U-Net

Diabetes Retinopathy is the primary reason for avoidable vision loss, especially among people of working age. Diabetes is expected to affect 600 million people worldwide by 2040, with one-third developing Diabetic Retinopathy [1] Recent studies have highlighted the need for more cost-effective and efficient methods of diagnosing, managing, and treating retinal diseases in clinical eye care settings. Before the development of a computer-aided diagnosis tool, the difficulty of achieving an accurate early diagnosis at an affordable cost must be considered. It may be possible to expand diabetic retinopathy screening efforts and allow physicians to use their time more efficiently by utilizing computer-aided disease detection in retinal image processing. Biomedical engineers and computer scientists now have a unique opportunity to meet the demands of clinical practice, thanks to advances in edge computing and machine learning techniques. We have used Enhanced U-Net to segment typical pathological features of DR which include hard exudates (EX), soft exudates (SE), microaneurysms (MA), and haemorrhages (HE) in the Indian Diabetic Retinopathy Image Dataset (IDRiD) with better accuracy than other techniques used in this domain. The Enhanced U-Net is able to segment all the features at an early stage, hence, this method can be used to aid diagnosis of DR at an early stage, thereby preventing vision loss at later stages.