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FAU Study Uses Raman Spectroscopy and AI to Detect Skin Cancer

Andrew Terentis, Ph.D., senior author, professor and chair of ������Ƶ Atlantic's Department of Chemistry and Biochemistry.

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Skin cancer is the most common cancer in the United States and among the most common worldwide. Nearly 1.5 million new cases were diagnosed globally in 2024, including nearly 340,000 melanomas. Nonmelanoma skin cancers – primarily basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) – are even more common, with 5.4 million cases diagnosed annually in the U.S.

Early detection is critical. However, distinguishing cancerous lesions from benign and precancerous growths can be challenging because many can look similar. A biopsy followed by microscopic examination of tissue remains the gold standard for diagnosis, but biopsies are invasive, costly and can sometimes be performed on lesions that ultimately prove to be benign.

There is a need for rapid, non-invasive diagnostic tools that can accurately identify skin cancer while potentially reducing unnecessary biopsies.

������Ƶ Atlantic University researchers are exploring a new way to detect skin cancer by combining Raman spectroscopy – a technique that provides a molecular “fingerprint” of tissue – with machine learning. Unlike conventional biopsy, Raman spectroscopy analyzes how light scatters when it interacts with molecules in tissue, providing information about its chemical composition without removing or specially preparing the tissue.

“The promise of this technology is that it could give clinicians another way to look beneath the surface of a skin lesion without immediately having to remove tissue,” said Andrew Terentis, Ph.D., senior author, professor and chair, Department of Chemistry and Biochemistry, FAU Charles E. Schmidt College of Science. “By combining the molecular information provided by Raman spectroscopy with machine learning, we are beginning to see how subtle differences in tissue chemistry can be used to distinguish cancer from normal skin.”

To test the approach, researchers used a mobile Raman spectroscopy system equipped with a 785-nanometer diode laser and handheld probe. They analyzed more than 50 clinical samples ex vivo, including BCC, SCC and normal skin. They generated nearly 1,000 Raman spectra and assessed a variety of machine-learning methods to determine how accurately the spectral data could distinguish among the three tissue types.

Results of the study, published in the as part of Advanced Chemical Microscopy for Life Science and Translational Medicine 2026, showed that several machine-learning approaches could identify patterns in Raman spectra that distinguish normal skin from cancerous tissue. K-nearest neighbors and support vector machine classifiers achieved the highest overall test accuracy, at about 84%. The support vector machine achieved 78.7% sensitivity and 88.6% specificity, while a shallow neural network achieved 80.8% accuracy and the highest receiver operating characteristic area under the curve, or ROC AUC, at 0.910.

The researchers first examined the Raman spectra to identify molecular patterns associated with the different tissue types. The analysis showed that normal tissue could be distinguished relatively well from the two types of skin cancer, while BCC and SCC showed greater overlap in their molecular signatures. Raman spectra from the cancerous samples tended to show stronger protein-related signals, while normal tissue showed stronger lipid-related signals, providing clues about the molecular characteristics that could help differentiate tissue types.

“Our results are preliminary, but they point toward a future in which a rapid, non-invasive measurement could help guide clinical decisions and potentially reduce unnecessary biopsies,” said Terentis.

The researchers are now looking toward larger studies, further optimization of the machine-learning models and more advanced approaches, including deep neural networks, which could improve diagnostic performance.

“Raman spectroscopy gives us a wealth of molecular information, but the challenge is teaching a computer to recognize which patterns matter most,” said Terentis. “With more samples and better-trained models, we believe there is significant potential to improve the accuracy of this approach. Ultimately, we want to develop technology that is not only accurate, but also practical, portable and accessible enough to become a useful tool in the clinical setting.”

The study demonstrates how advances in optical spectroscopy and machine learning could provide a new path toward faster, non-invasive skin cancer assessment, potentially complementing conventional biopsy and helping clinicians make more informed decisions about which lesions require further evaluation.

Study co-authors are Venkata Dhulipalla, a former FAU graduate student; John Strasswimmer, M.D., Ph.D., an affiliate professor of dermatology and research biochemistry in FAU’s Charles E. Schmidt College of Medicine; and FAU undergraduate students Phuong Nguyen, Lizzie Klein and Max McCain.

-FAU-

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