Comparative evaluation of conventional color imaging and hyperspectral imaging data as inputs to machine learning algorithms for classifying burn severity

Samagra Pandey; Gordon T. Kennedy; Rebecca A. Rowland; Victor C. Joe; Theresa L. Chin; Robert J. Christy; David M. Burmeister; Robert H. Wilson; Anthony J Durkin

doi:10.1117/12.2664961

17 March 2023 Comparative evaluation of conventional color imaging and hyperspectral imaging data as inputs to machine learning algorithms for classifying burn severity

Samagra Pandey, Gordon T. Kennedy, Rebecca A. Rowland, Victor C. Joe, Theresa L. Chin, Robert J. Christy, David M. Burmeister, Robert H. Wilson, Anthony J Durkin

Author Affiliations +

Proceedings Volume PC12352, Photonics in Dermatology and Plastic Surgery 2023; PC1235207 (2023) https://doi.org/10.1117/12.2664961
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Accurately classifying burn severity is crucial to inform proper treatment. Here, we quantitatively compare the efficacy of machine learning (ML) burn classification algorithms using multispectral imaging versus conventional digital color imaging data as inputs. We imaged 80 porcine burns that underwent biopsy and histology for ground truth categorization into “skin graft needed” versus “no graft needed” groups. The accuracy of our ML algorithm with a transfer learning architecture was 97.5% for the multispectral model, 57.5% for the digital-color model, and 57.5% for the multispectral+digital-color model. This result strongly supports the use of multispectral imaging over digital-color imaging for burn classification.

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Samagra Pandey, Gordon T. Kennedy, Rebecca A. Rowland, Victor C. Joe, Theresa L. Chin, Robert J. Christy, David M. Burmeister, Robert H. Wilson, and Anthony J Durkin "Comparative evaluation of conventional color imaging and hyperspectral imaging data as inputs to machine learning algorithms for classifying burn severity", Proc. SPIE PC12352, Photonics in Dermatology and Plastic Surgery 2023, PC1235207 (17 March 2023); https://doi.org/10.1117/12.2664961

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