Intelligent System for Diagnosing Vestibular Schwannoma

Authors

  • Victor Sineglazov State University "Kyiv Aviation Institute" https://orcid.org/0000-0002-3297-9060
  • Andrew Sheruda National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
  • Maksym Shevchenko State University "Kyiv Aviation Institute"

DOI:

https://doi.org/10.18372/1990-5548.85.20426

Keywords:

vestibular schwannoma, diagnosis of schwanoma growth, MRI imaging, texture analysis, classification task

Abstract

This scientific work is devoted to the development of an intelligent system for the diagnosis of vestibular schwannoma. A new approach to the texture analysis of magnetic resonance images of vestibular schwannoma is proposed in order to determine the assessment of tumor growth. The use of this approach will prevent the risks of tumor progression and timely determine the need for surgical intervention. Several classes of texture descriptors were used in the study, including: first-order statistics (intensity histograms), gray level co-occurrence matrix, gray level run length matrix, gray level size zone matrix, gray level dependency matrix, as well as wavelet-transformed features. The complex use of these descriptors made it possible to formalize the internal microstructure of the tumor and implement an effective model for predicting its growth.

Author Biographies

Victor Sineglazov , State University "Kyiv Aviation Institute"

Doctor of Engineering Science

Professor

Head of the Department Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Andrew Sheruda , National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Master's degree

Department of Artificial Intelligence, Institute of Applied Systems Analysis

Maksym Shevchenko, State University "Kyiv Aviation Institute"

Postgraduate Student

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation Electronics and Telecommunications

References

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Published

2025-09-29

How to Cite

Sineglazov , V., Sheruda , A., & Shevchenko, M. (2025). Intelligent System for Diagnosing Vestibular Schwannoma. Electronics and Control Systems, 3(85), 9–17. https://doi.org/10.18372/1990-5548.85.20426

Issue

Vol. 3 No. 85 (2025)

Section

COMPUTER SCIENCE

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