Deep learning models for retinal blood vessels segmentation: A review. Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. Kwasigroch, A., Jarzembinski, B., & Grochowski, M. Fully automated detection and quantification of macular fluid in OCT using deep learning. Deep-learning based automated segmentation of macular edema in optical coherence tomography. The results of implementation of the Architectures showed that Vgg16 showed better results in detecting the images rather than Dense Net Architecture with an accuracy percentage of 97.53% approximately a percent greater than Dense Net. After the detection of the disease, the diseased OCT images are segmented from the background for the Region of Interest detection using Python OpenCV library that is used for the processing of images. DenseNet and Vgg16 Architectures of Deep Learning were used in the study and the hyper parameters of both of the architectures were changed to diagnose the disease properly. Among the few attributes of CNV, the bigger turning point of veins is a moderately clear element, so we will utilize this property to see if there is CNV in an OCTA picture. This paper proposes a technique for finding CNV in OCTA pictures.

In CNV the Retinal Pigment Epithelial layer experiences changes in various properties which can be related to the assistance of OCT Images. OCT images can be used to differentiate between a healthy eye and an eye with CNV disease. In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures.

Normal retina oct image