Intellectual System Diagnostics Glaucoma

Authors

DOI:

https://doi.org/10.26555/jiteki.v9i4.26969

Keywords:

Eyes, Glaucoma, Optic nerve, Ophthalmology, Optical coherence tomography

Abstract

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma.

Author Biographies

Parul Ichhpujani, Govt. Medical College and Hospital, Chandigarh, India

Professor at Glaucoma Services,

Department of Ophthalmology

Vladimir Ryabtsev, Cherkassy Branch of Private Higher Education Establishment "European University"

Doctor of Technical Sciences (Dr. Tech. Sc.), Professor, Professor
Department of Mathematics, Computer Science and Social Sciences and Humanities

Tetyana Yuriyivna Utkina, Cherkasy State Technological University

Ph.D. (in Computer Science), Associate Professor, Associate Professor
Department of Robotics and Specialized Computer Systems

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Published

2023-11-10

How to Cite

[1]
P. Ichhpujani, V. Ryabtsev, and T. Y. Utkina, “Intellectual System Diagnostics Glaucoma”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 9, no. 4, pp. 1045–1059, Nov. 2023.

Issue

Vol. 9 No. 4 (2023): December

Section

Articles

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