Open Access

ARTICLE

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

D. Stalin David^1,*, S. Arun Mozhi Selvi², S. Sivaprakash³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵, Pankaj Dadheech⁶, Sudhakar Sengan⁷

1 Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, 605108, Tamil Nadu, India
2 Department of Computer Science Engineering, DMI St. John the Baptist University, Mangochi, Lilongwe, Malawi
3 Department of Information Technology, CMR Engineering College (Autonomous), Hyderabad, 501401, Telangana, India
4 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, 638060, Tamil Nadu, India
5 Department of Mathematics, Jaypee University of Engineering and Technology, Guna, 473226, Madhya Pradesh, India
6 Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, 302017, Rajasthan, India
7 Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627152, Tamil Nadu, India

* Corresponding Author: D. Stalin David. Email:

Computers, Materials & Continua 2022, 70(2), 2563-2579. https://doi.org/10.32604/cmc.2022.020059

Received 07 May 2021; Accepted 17 June 2021; Issue published 27 September 2021

Abstract

Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network (CNN) classification for classifying Glaucoma or Diabetes Retinopathy (DR). The detection of the boundary between the OC and the OD is performed by the SBEFCM, which is the latest weighted ellipse fitting model. The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here. There is a pre-processing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels. The ascertaining of OC and OD boundary, which characterized many output factors for glaucoma detection, has been developed by Ensemble CNN classification, which includes detecting sensitivity, specificity, precision, and Area Under the receiver operating characteristic Curve (AUC) values accurately by an innovative SBEFCM. In terms of contrast, the proposed Ensemble CNN significantly outperformed the current methods.

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