International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Ankita Ghorui1, Subhasri Chatterjee1, Roshan Makkar2, Arulmozhivarman Pachiyappan1, Balamurugan S1*

1 School of Electrical Engineering, Vellore Institute of Technology, Vellore, 632014, India

2 Society of Applied Microwave Electronics Engineering and Research (SAMEER), Mumbai, India

 

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ABSTRACT

Detection of glaucoma has become critical, as it has arisen as the subsequent essential driver of visual impairment, around the world. At present, most of the algorithms in use rely on pre-trained deep neural networks to produce the best results. However, the high computational time and complexity and the need of a large database, make glaucoma-detection arduous and difficult. Keeping these in mind, this paper proposes a new convolutional neural network architecture, in particular, ProspectNet, which has demonstrated to accomplish a better accuracy with lesser computational time and complexity when tested against two pre-trained networks: VGG16 and DenseNet121. The data set is an amalgamation of two publicly available datasets- DRISHTI-GS and Glaucoma Dataset (Kaggle), comprising ocular colour fundus images of glaucomatous as well as normal eyes. ProspectNet has accomplished a normal AUC (area under the curve) as 0.991, specificity, and precision as 0.98. Confusion matrices also plotted to illustrate the new architecture’s efficacy. These outcomes demonstrate that ProspectNet is a hearty option in contrast to other best in class calculations for a medium sized dataset. The paper suggests three distinct structures for glaucoma detection. One advantage of our approach is that no special feature selection, such as detailed measurements of particular traits like the structure of the optic nerve head, is necessary.


Keywords: Glaucoma, Ocular colour fundus images, Deep convolutional neural networks.


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ARTICLE INFORMATION

Received: 2022-08-24
Revised: 2022-11-20
Accepted: 2023-02-07
Available Online: 2023-02-24


Cite this article:

Ghorui, A., Chatterjee, S., Makkar, R., Pachiyappan, A., Balamurugan, S. Deployment of CNN on colour fundus images for the automatic detection of glaucoma. International Journal of Applied Science and Engineering, 20, 2022202. https://doi.org/10.6703/IJASE.202303_20(1).003

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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