Diabetic Retinopathy Image Lesion Segmentation with Feature Fusion Relation Transformer Network

Diabetes is a common disease that affects different vital organs of the human body, including the eyes. In diabetic patients, a change in blood sugar level leads to eye problems. Around 80% of the patients who have diabetes for more than 10 years have severe eye-related pathological disorders such as retinopathy and maculopathy. Proper detection, diagnosis, and treatment of eye-related pathologies prevent damage to the eye during the earliest stages of diabetic disease—the developed stage findings in patients losing their vision. The retinal damage due to diabetes is termed Diabetic Retinopathy (DR). The treatment of DR involves detecting the presence of the disease in the form of microaneurysms (MA), hemorrhages (HE), and exudates (EX) in the retinal area. The process of segmenting a massive segment of Retinal Images (RI) performs a prominent role in DR classification. The existing research concentrates on Optic Disc (OD) segmentation. This article focuses on the segmentation of MA, HE, and EX using a Feature Fusion Relation Transformer Network (FFRTNet). In this research, the benchmark dataset, the Indian Diabetic Retinopathy Image Dataset (IDRID), is used for the ablation study to evaluate the use of every module. The proposed method, FFRTNet, is compared with state-of-the-art methods. The evaluation of FFRTNet enhances the segmentation by 3.56%, 4.34%, and 3.75% on metrics, namely sensitivity, Intersection-over-Union (IoU), and Dice coefficient (DICE). The qualitative and quantitative results proved the superiority of FFRTNet in segmenting lesions in DR.