Effective cervical intraepithelial neoplasia classification through polarization-based machine learning

Jingyu Ren, Yanqiu Li*, Ke Liu, Yuanhe Li, Aijun Liu, Ziyu Ma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Identification of cervical intraepithelial neoplasia (CIN) tissues holds significant clinical importance in reducing the incidence and mortality of cervical cancer. The ultraviolet Mueller matrix imaging polarimeter (UV-MMIP) can significantly enhance morphological specific contrast. For the first time, to our best knowledge, we used polarimetric metrics measured by UV-MMIP combined with machine-learning techniques to achieve high-accuracy CIN classification. Initially, eight classifiers of four types were trained, and the best-performing one was identified, achieving a classification accuracy (F1 score) of up to 0.815. Subsequently, the best classifier from each type was assembled to construct a stacked model to further explore the potential of the machine-learning techniques, resulting in an improved F1 score of 0.838. Additionally, the classification results indicate that depolarization-related metrics have stronger discriminatory power on the trained classifier’s outcome than retardance-related metrics, which highlights the contribution of UV-MMIP for the classification task. Our work validates the feasibility of the polarization-based machine-learning framework for effective CIN classification.

Original languageEnglish
Pages (from-to)7499-7509
Number of pages11
JournalApplied Optics
Volume63
Issue number28
DOIs
Publication statusPublished - 1 Oct 2024

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Ren, J., Li, Y., Liu, K., Li, Y., Liu, A., & Ma, Z. (2024). Effective cervical intraepithelial neoplasia classification through polarization-based machine learning. Applied Optics, 63(28), 7499-7509. https://doi.org/10.1364/AO.525429