Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN

• Published in: Wireless personal communications Vol. 127; no. 3; pp. 2569 - 2583
• Main Authors: Abirami, M. S., Vennila, B., Suganthi, K., Kawatra, Sarthak, Vaishnava, Anuja
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V
• Subjects: Communications Engineering; Computer Communication Networks; Deep learning; Engineering; Medical imaging; Networks; Optical Coherence Tomography; Signal,Image and Speech Processing; Deep learning; CNN; Retina OCT; Vgg16 model; DenseNet model
• ISSN: 0929-6212; 1572-834X
• DOI: 10.1007/s11277-021-09086-8

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