Publication Type : Journal Article
Publisher : Springer Netherlands
Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 30, p.631-637 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060203449&doi=10.1007%2f978-3-030-00665-5_62&partnerID=40&md5=33e6189aa085b583bf403bea454dbb36
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : This paper addresses automated glaucoma detection system using pre-trained convolutional neural networks (CNNs). CNNs, a class of deep neural networks (DNNs), extract features of high-level abstractions from the fundus images, thereby eliminating the need for hand-crafted features which are prone to inaccuracies in segmenting landmark regions and require excessive involvement of experts for annotating these landmarks. This work investigates the applicability of pre-trained CNNs for glaucoma diagnosis, which is preferred when the dataset size is small. Further, pre-trained networks have the advantage of the quick model building. The proposed system has been validated on the High-Resolution (HRF), which is a publicly available benchmark database. Results demonstrate that among other pre-trained CNNs, VGG16 network is more suitable for glaucoma diagnosis. © Springer Nature Switzerland AG 2019.
Cite this Research Publication : M. Sushil, Suguna, G., Dr. Lavanya R., and M. Devi, N., “Performance comparison of pre-trained deep neural networks for automated glaucoma detection”, Lecture Notes in Computational Vision and Biomechanics, vol. 30, pp. 631-637, 2019.