This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model’s robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions.
本研究开创性地将人工智能（AI）与高光谱成像（HSI）技术应用于皮肤癌病变的诊断，重点聚焦于蕈样肉芽肿（MF）及其与银屑病（PsO）和特应性皮炎（AD）的鉴别诊断。通过利用包含MF、PsO、AD及正常皮肤共1659张皮肤图像的综合数据集，采用新型多帧AI算法进行计算机辅助诊断。研究进一步运用U-Net Attention模型和XGBoost算法等先进技术，将图像从色彩空间转换至光谱域，实现了皮肤病变的自动分割与分类。该成果凸显了AI与HSI在皮肤科诊断中的潜力，为传统方法提供了一种无创、高效且精准的替代方案。研究结果对MF早期浸润性病变的检测尤为重要，模型在病变分割与分类中表现出稳健性能，其卓越的预测准确性通过k折交叉验证得到证实。当k折交叉验证值为7时，模型达到最佳性能：灵敏度90.72%、特异度96.76%、F1分数90.08%、ROC-AUC值0.9351。本研究标志着皮肤科诊断领域的重大进展，为皮肤恶性肿瘤及炎症性疾病的早期精准识别作出了重要贡献。
Identification of Skin Lesions by Snapshot Hyperspectral Imaging
肿瘤(癌症)患者之家