# Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors

## TL;DR

This study introduces a new Raman imaging method to accurately diagnose skin tumors in dogs and cats using machine learning and bimodal imaging.

## Contribution

A novel bi-modal Raman imaging technique is proposed for veterinary oncology with high diagnostic accuracy.

## Key findings

- Machine learning classifiers like SVM and DT achieved 85–95% accuracy in distinguishing tumor types.
- Bimodal imaging combining Raman and NIR autofluorescence improved diagnostic performance significantly.
- Rapid, safe imaging at 1437 cm−1 and 1655 cm−1 enabled accurate classification of malignant and benign tissues.

## Abstract

This study introduces Raman imaging technique for diagnosing skin cancer in veterinary oncology patients (dogs and cats). Initially, Raman spectral bands (with specificity to certain molecular structures and functional groups) were identified in formalin-fixed samples of mast cell tumors and soft tissue sarcomas, obtained through routine veterinary biopsy submissions. Then, a custom-built Raman macro-imaging system featuring an intensified CCD camera (iXon Ultra 888, Andor, UK), tunable narrow-band Semrock (USA) optical filter compartment was used to map the spectral features at 1437 cm−1 and 1655 cm−1 in ex vivo tissue. This approach enabled wide-field (cm2), rapid (within seconds), and safe (< 400 mW/cm2) imaging conditions, supporting accurate diagnosis of tissue state. The findings indicate that machine learning classifiers – particularly support vector machine (SVM) and decision tree (DT) – effectively distinguished between soft tissue sarcoma, mastocytoma and benign tissues using Raman spectral band imaging data. Additionally, combining Raman macro-imaging with residual near-infrared (NIR) autofluorescence as a bimodal imaging technique enhanced diagnostic performance, reaching 85 – 95% in accuracy, sensitivity, specificity, and precision – even with a single spectral band (1437 cm−1 or 1655 cm−1). In conclusion, the proposed bi-modal imaging is a pioneering method for veterinary oncology science, offering to improve the diagnostic accuracy of malignant tumors.