Oral cancer diagnosis via machine learning and micro-FTIR hyperspectral imaging
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DANIELA DE FATIMA TEIXEIRA DA SILVA
GLEICE CONCEICAO MENDONCA GERMANO
DENISE MARIA ZEZELL
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Journal of Physics: Conference Series
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Oral squamous cell carcinoma (OSCC) remains a major global health challenge, and its early detection is essential for improving patient prognosis. Fourier-transform infrared (FTIR) hyperspectral imaging offers a powerful, label-free approach for probing biochemical alterations in biological tissues, providing rich spectral information that can support computational diagnosis. In this study, we evaluated the performance of a Random Forest classifier applied to FTIR hyperspectral images of OSCC and control oral tissues. After rigorous preprocessing and restriction of the analysis to the 1500-1750 cm−1 region—the portion of the fingerprint band with highest discriminative variability—the model achieved strong pixel-level performance, with high precision, recall, and an area under the ROC curve of 0.986. Image-level classification, a clinically relevant metric, yielded an accuracy of 0.86 across 96 samples, demonstrating the model’s reliability in assigning whole-sample labels. Feature importance analysis identified key vibrational modes associated with malignant transformation, notably the Amide I and Amide II bands, as well as lipid-related C=O stretching near 1735 cm−1. These findings indicate that Random Forest, combined with FTIR hyperspectral imaging, can robustly capture biologically meaningful spectral signatures and represents a promising complementary tool for OSCC screening and diagnostic support.
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PERES, D. L.; SILVA, D. F. T.; GERMANO, G. C. M.; PEREIRA, T. M.; FELIPE, J. C.; MATOS, L. L. de; ZEZELL, D. M. Oral cancer diagnosis via machine learning and micro-FTIR hyperspectral imaging. Journal of Physics: Conference Series, v. 3183, p. 1-10, 2026. DOI: 10.1088/1742-6596/3183/1/012003. Disponível em: https://repositorio.ipen.br/handle/123456789/50218. Acesso em: 08 Oct 2026.
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