Detection of Choroidalneovascularization (Cnv) in Retina Oct Images Using Vgg16 and Densenet Cnn

In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. Optical Coherence Tomography (OCT) images can be used to differentiate between a healthy eye and an eye with CNV disease. 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. 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. 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.