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Soft Computing Based Discriminator Model for Glaucoma Diagnosis

Anisha Rebinth1,*, S. Mohan Kumar2

1 Department of Information Science and Engineering, NHCE, Visvesvaraya Technological University, Bangalore, India
2 Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore, India

* Corresponding Author: Anisha Rebinth. Email: email

Computer Systems Science and Engineering 2022, 42(3), 867-880.


In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel’s intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequency domain features from LAWS are generated from the corresponding ROI for further classification using kernel based Support Vector Machine (SVM). The DMGD system performances are validated using four fundus image databases; ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels; Linear Kernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel, Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernel based classifies from the properly segmented ROI.


Cite This Article

A. Rebinth and S. Mohan Kumar, "Soft computing based discriminator model for glaucoma diagnosis," Computer Systems Science and Engineering, vol. 42, no.3, pp. 867–880, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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