Work

Intelligent System for Diagnosing Vestibular Schwannoma

Year: 2025

Type: article

Abstract: This scientific work is dedicated to the development of an intelligent system for diagnosing vestibular schwannoma. A new approach to texture analysis of magnetic resonance imaging images of schwanomas has been proposed as a method for assessing the growth of swelling. The use of this approach will help to avoid the risks of the progression of the neoplasm and immediately eliminate the need for surgical intervention. At the boundaries of the research, a number of classes of texture descriptors were put together, including: first-order statistics (intensity histograms), grey-color consistency matrix, dovzhin sequence matrix Gray Rivne, zone size matrix, Gray Rivn deposit matrix, as well as hvillet-transformed signs. The comprehensive analysis of these descriptors made it possible to formalize the internal microstructure of the fluff and implement an effective model for predicting its growth.

Source: Electronics and Control Systems

DOI: 10.18372/1990-5548.85.20426

Authors: Victor Sineglazov, Volodymyr Fedirko, Vasyl Shust, Andrew Sheruda, Maksym Shevchenko

Institutions: National University "Kyiv Aviation Institute", Romodanov Neurosurgery Institute of the National Academy of Medical Sciences of Ukraine, National Academy of Medical Sciences of Ukraine, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4415114659

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