Early-stage detection of cutaneous melanoma can vastly increase the chances of cure. Excision biopsy followed by histological examination is considered the gold standard for diagnosing the disease, but requires long high-cost processing time, and may be biased, as it involves qualitative assessment by a professional. In this paper, we present a new machine learning approach using raw data for skin Raman spectra as input. The approach is highly efficient for classifying benign versus malignant skin lesions (AUC 0.98, 95% CI 0.97–0.99). Furthermore, we present a high-performance model (AUC 0.97, 95% CI 0.95–0.98) using a miniaturized spectral range (896–1039 cm⁻¹), thus demonstrating that only a single fragment of the biological fingerprint Raman region is needed for producing an accurate diagnosis. These findings could favor the future development of a cheaper and dedicated Raman spectrometer for fast and accurate cancer diagnosis.

皮肤黑色素瘤的早期检测可以大大增加治愈的机会。切除活检和组织学检查被认为是诊断疾病的金标准，但需要很长的高成本处理时间，并且可能存在偏见，因为它涉及专业人士的定性评估。在本文中，我们提出了一种新的机器学习方法，使用皮肤拉曼光谱的原始数据作为输入。该方法对于分类良性和恶性皮肤病变非常有效（AUC 0.98，95% CI 0.97–0.99）。此外，我们使用小型化光谱范围 (896–1039 cm ^-1)，从而证明仅需要生物指纹拉曼区域的单个片段即可进行准确诊断。这些发现可能有利于未来开发更便宜且专用的拉曼光谱仪，用于快速准确地诊断癌症。