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This paper presents an innovative algorithm developed for the morphological analysis of brain images, which is applied in the field of differential diagnosis of primary extra-axial tumours using magnetic resonance imaging. The study discusses the basic principles of radiomics and the key steps that play an important role in the diagnostic process of extra-axial tumours. It also analyses the importance of geometric features in the differential diagnosis of extra-axial tumours based on magnetic resonance imaging data. The results obtained showed a high classification accuracy of 97.74% and the area under the receiver operating characteristic curve for the proposed feature set was 0.97.

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Algorithm for Evaluating Morphological Features of Magnetic Resonance Images of Primary Extra-Axial Brain Tumors for Their Differential Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • Nataly Ilyasova,
  • Alisa Selezneva,
  • Nikita Demin,
  • Aleksandr Kapishnikov,
  • Evgeniy Surovcev

摘要

This paper presents an innovative algorithm developed for the morphological analysis of brain images, which is applied in the field of differential diagnosis of primary extra-axial tumours using magnetic resonance imaging. The study discusses the basic principles of radiomics and the key steps that play an important role in the diagnostic process of extra-axial tumours. It also analyses the importance of geometric features in the differential diagnosis of extra-axial tumours based on magnetic resonance imaging data. The results obtained showed a high classification accuracy of 97.74% and the area under the receiver operating characteristic curve for the proposed feature set was 0.97.