Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods

Svetlana Valjarevic, Milan B. Jovanovic, Nenad Miladinovic, Jelena Cumic, Stefan Dugalic, Peter R. Corridon, Igor Pantic

    Research output: Contribution to journalArticlepeer-review

    9 Scopus citations

    Abstract

    Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

    Original languageBritish English
    Pages (from-to)1220-1227
    Number of pages8
    JournalMicroscopy and Microanalysis
    Volume29
    Issue number3
    DOIs
    StatePublished - Jun 2023

    Keywords

    • cancer
    • larynx
    • microscopy
    • morphology
    • nucleus

    Fingerprint

    Dive into the research topics of 'Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods'. Together they form a unique fingerprint.

    Cite this