A Novel IR Analyzer Based Property Extraction for Segmented Branch Retinal Artery Occlusion and GWO-CNN Based Classification – An Ophthalmic Outcome

Abstract

Branch Retinal Artery Occlusion (BRAO) has become a serious disorder causing permanent loss of vision. It is mainly caused due to the rupture of blood vessels which is too small in size and thereby increasing the risk for efficient detection. Therefore an efficient detection scheme is necessary to evaluate appropriately and pave the way for systemic therapy to preserve or recover vision in the affected eye. In this paper, a fully automatic detection scheme has been developed for accurate segmentation followed by classification of BRAO volumes using the fundus images. Coming to the point this is the first automatic classification framework using an optimized neural network for classifying BRAO. It's mainly a three-step process where the first step concentrates on removing noise and thereby mproving the quality of fundus image. According to which an boosted anisotropic diffusionbased enhancement filter is used here, which effectively removes the noise. This is justified using image quality metrics. Second step here involves adaptive cluster with super pixel segmentation which clearly segments the affected area with high contrast. For the segmented image IR Analyzer is used for extracting region properties. In the final step gray wolf optimization-based convolution neural network (GWO-CNN) classifier is used. Here GWO-CNN classifier is used to differentiate the normal and abnormal images. The effectiveness of the above method is evaluated and obtained an accuracy of 98.57% when it is implemented in MATLAB 2018 software.

Keywords:

• Adaptive cluster with super pixel segmentation
• Boosted anisotropic diffusion filter
• Branch retinal artery occlusion (BRAO)
• CNN
• Gray wolf optimization based convolution neural network (GWO-CNN)
• Fundus image

ACKNOWLEDGEMENTS

The authors first of all take this opportunity to thank the management of Arunachala College of Engineering for Women, for the continuous and sincere support throughout this work. Then the authors like to thank Bejansingh Eye Hospital, Nagarcoil for validating and providing the database without which the work has become impossible. Finally we thank the reviewers who helped in increasing the quality of the paper through their valuable comments.

Additional information

Notes on contributors

S. G. Gayathri

S G Gayathri ME, (PhD), is a registered research scholar of Anna Univeristy, Chennai, who is currently working for her PhD degree in Arunachala College of Engineering for Women. She received the Bachelor’s degree in electronics and communication engineering from Anna University Chennai and the ME degree in communication systems from Anna University, Chennai. She published journal and conference papers and also participated in international conferences. Her areas of interest include medical image processing, image segmentation and image classification.

S. Joseph Jawhar

S Joseph Jawhar received PhD degree from Anna university Chennai in 2009. From 1990 to 1991, he was an associate lecturer and from 1994 to 1998, he was lecturer in EEE and from 1998 to 2001, he was an assiatant professor and HOD for EEE all in Noorul Islam College of Engineering, Kumaracoil and in 2009, he served as controller of examination for Noorul Islam University. Currently, he is working as principal/professor Department of EEE in Arunachala College of Engineering for Women Manavilai. His areas of interest include power electronics and soft computing. Email: [email protected]