Machine learning-based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid

Kupriyanov V, Blondel W, Daul C, Hohmann M, Khairallah G, Kistenev Y, Amouroux M (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Journal of Biomedical Optics Society of Photo-optical Instrumentation Engineers (SPIE)

Book Volume: 30

Pages Range: 035001-

Journal Issue: 3

DOI: 10.1117/1.JBO.30.3.035001

Abstract

Significance: The incidence of keratinocyte carcinomas (KCs) is increasing every year, making the task of developing new methods for KC early diagnosis of utmost medical and economical importance. Aim: We aim to evaluate the KC diagnostic aid performance of an optical spectroscopy device associated with a machine-learning classification method. Approach: We present the classification performance of autofluorescence and diffuse reflectance optical spectra obtained in vivo from 131 patients on four histological classes: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), actinic keratosis (AK), and healthy (H) skin. Classification accuracies obtained by support vector machine, discriminant analysis, and multilayer perceptron in binary- and multi-class modes were compared to define the best classification pipeline. Results: The accuracy of binary classification tests was formula presented to discriminate BCC or SCC from H. For AK versus other classes, the classification achieved a 65% to 75% accuracy. In multiclass (three or four classes) classification modes, accuracy reached 57%. Fusion of decisions increased classification accuracies (up to 10 percentage point-increase), proving the interest of multimodal spectroscopy compared with a single modality. Conclusions: Such levels of classification accuracy are promising as they are comparable to those obtained by general practitioners in KC screening.

Authors with CRIS profile

Martin Hohmann Lehrstuhl für Photonische Technologien (LPT)

Involved external institutions

Université de Lorraine FR France (FR) Le Centre Hospitalier Régional de Metz-Thionville FR France (FR) National Research Tomsk State University RU Russian Federation (RU)

How to cite

APA:

Kupriyanov, V., Blondel, W., Daul, C., Hohmann, M., Khairallah, G., Kistenev, Y., & Amouroux, M. (2025). Machine learning-based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid. Journal of Biomedical Optics, 30(3), 035001-. https://doi.org/10.1117/1.JBO.30.3.035001

MLA:

Kupriyanov, Valentin, et al. "Machine learning-based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid." Journal of Biomedical Optics 30.3 (2025): 035001-.

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