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: Journal of biomedical optics

PMID: 40041369

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.

Authors

Valentin Kupriyanov

Université de Lorraine, CNRS, CRAN UMR, Vandoeuvre-Lès-Nancy, France.
Walter Blondel

CRAN UMR 7039, University of Lorraine, CNRS, Nancy, France.
Christian Daul

CRAN UMR 7039, University of Lorraine, CNRS, Nancy, France.
Martin Hohmann

Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Institute of Photonic Technologies (LPT), Erlangen, Germany.
Grégoire Khairallah

Metz-Thionville Regional Hospital, Department of Plastic, Aesthetic and Reconstructive Surgery, Ars-Laquenexy, France.
Yury Kistenev

Tomsk State University, Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk, Russia.
Marine Amouroux

Université de Lorraine, CNRS, CRAN UMR, Vandoeuvre-Lès-Nancy, France.

Keywords

Aged Aged, 80 and over Carcinoma, Basal Cell Carcinoma, Squamous Cell Diagnosis, Computer-Assisted Discriminant Analysis Female Humans Keratosis, Actinic Machine Learning Male Middle Aged Optical Imaging Skin Skin Neoplasms Spectrometry, Fluorescence Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40041369)

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

Abstract

Authors

Keywords

External Resources

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