Segmentation of Hard Exudates in Retina Fundus Images Using BCDU-Net

The importance of Diabetic Retinopathy (DR) screening requires attention to the development of computer-aided diagnostic tools. Computer-Aided Diagnosis (CAD) of retinal detachment imaging can reduce mass screening of the diabetic population. For this purpose, the deep learning technique and the developed U-Net canonization network have been used. Using this network, it receives retinal images and shows the segmentation of the hard exudate lesion as a binary image. The result of this research has been evaluated on the IDRID dataset with three important indicators of dice coefficient, sensitivity, and accuracy achieve at 76.81%, 72.24%, and 99.30%, respectively, and the effectiveness of the approach was confirmed.