ARCHITECTURE AND PERFORMANCE OF GLAUCOMA DETECTION SYSTEM ON THE BASIS COMBINING CLASSIFIERS

Authors

  • Sreedhar M. UG Student, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai - 602105, India Author
  • Dr. Radhika Bhaskar Associate Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai - 602105, India Author

DOI:

https://doi.org/10.61841/j9x1ef59

Keywords:

Fundus Image, Glaucoma Detection, Optic Cup, Optic Disc, Combining Classifiers

Abstract

Glaucoma is a category of eye disorders that are critical to good vision, destroying the optic nerve. The effect is often an abnormally high eye pressure. Glaucoma is one of the main blindness causes for people over 60 years old. It is more common in older adults, though, at all ages. The early diagnosis of glaucoma is required. In this study, the Glaucoma Detection System (GDS) using combining classifiers is presented. The GAD system uses Naïve Bayes (NB) and Random Forest (RF) classifiers for the glaucoma detection. The optic cup and optic disc are mainly used to detect the abnormalities in the fundus images. Initially, the given input fundus images—optic cup and optic disc—are extracted and Region of Interest (ROI) is detected. Then the energy features are extracted and stored in the database. Then the extracted features are used as the input to predict the NB and RF classifiers, which are used as combining classifiers for glaucoma detection. 

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References

1. Atheesan S., Yashothara S., “Automatic glaucoma detection by using funduscopic images,” In 2016 International Conference on Wireless Communications, Signal Processing, and Networking (WiSPNET), 2016 Mar 23 (pp. 813-817). IEEE.

2. Kannan KG, Ganeshbabu TR., “Glaucoma image classification using discrete orthogonal stockwell transform,” Int. J. Adv. Sig. Img. Sci., 2017;3(1):6.

3. Nikam SM, Patil CY, “Glaucoma detection from fundus images using Matlab GUI,” in In 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA) (Fall), 2017 Sep 15 (pp. 1-4). IEEE.

4. Srinivasan C, Dubey S, Ganeshbabu TR, “Complex Texture Features for Glaucoma Diagnosis Using Support Vector Machine,” International Journal of MC Square Scientific Research, 2015 Dec 16;7(1):81-92.

5. Roslin M., Sumathi S., “Glaucoma screening by the detection of blood vessels and optic cup to disc ratio.” In 2016 International Conference on Communication and Signal Processing (ICCSP), 2016 Apr 6 (pp. 2210-2215). IEEE.

6. Ganeshbabu TR, "Computer-aided diagnosis of glaucoma detection using digital fundus image." International journal of advances in signal and image sciences, 2015 Dec 31;1(1):1-1.5.

7. Ahmad H, Yamin A, Shakeel A, Gillani SO, Ansari U, “Detection of glaucoma using retinal fundus images,” in 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE), 2014 Apr 22 (pp. 321-324). IEEE.

8. Belghith A, Balasubramanian M, Bowd C, Weinreb RN, Zangwill LM., “Glaucoma progression detection using variational expectation maximization algorithm,” In 2013 IEEE 10th International Symposium on Biomedical Imaging 2013 Apr 7, (pp. 876-879). IEEE.

9. Maharaja D, Shaby M., “Empirical Wavelet Transform and GLCM Features Based Glaucoma Classification from Fundus Image,” in International Journal of MC Square Scientific Research, 2017 Mar 27;9(1):78-85.

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Published

30.04.2020

Issue

Vol. 24 No. 2 (2020): 24 (2) 2020

Section

Articles

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Copyright (c) 2020 AUTHOR

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How to Cite

M. , S., & Bhaskar, R. (2020). ARCHITECTURE AND PERFORMANCE OF GLAUCOMA DETECTION SYSTEM ON THE BASIS COMBINING CLASSIFIERS. International Journal of Psychosocial Rehabilitation, 24(2), 5677-5683. https://doi.org/10.61841/j9x1ef59