Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning

Diabetic retinopathy (DR) is also called diabetic eye disease, which causes damage to the retina due to diabetes mellitus and that leads to blindness when the disease reaches an extreme stage. The medical tests take a lot of procedure, time, and money to test for the proliferative stage of diabetic retinopathy (PDR). Hence to resolve this problem, this model is proposed to detect and identify the proliferative stages of diabetic retinopathy which is also identified by its hallmark feature that is neovascularization. In the proposed system, the paper aims to correctly identify the presence of neovascularization using color fundus images. The presence of neovascularization in an eye is an indication that the eye is affected with proliferative PDR. Neovascularization is the development of new abnormal blood vessels in the retina. Since the occurrence of neovascularization may lead to partial or complete vision loss, timely and accurate prediction is important. The aim of the paper is to propose a method to detect the presence of neovascularization which involves image processing methods such as resizing, green channel filtering, Gaussian filter, and morphology techniques such as erosion and dilation. For classification, the different layers of CNN have been used and modeled together in a VGG-16 net architecture. The model was trained and tested on 2200 images all together from the Kaggle database. The proposed model was tested using DRIVE and STARE data sets, and the accuracy, specificity, sensitivity, precision, F1 score achieved are 0.96, 0.99, 0.95, 0.99, and 0.97, respectively, on DRIVE and 0.95, 0.99, 0.9375, 0.96, and 0.95, respectively, on STARE.