用多光谱超表面+混合模型实现高精度无创皮肤癌检测
Intelligent Skin Cancer Detection Using a Multispectral Metasurface and a Hybrid
- 用超表面捕捉可见光外的组织细微光谱变化
- 准确率98%、敏感度95%、特异度99%
- 适合临床便携诊断系统开发，结果可解释
皮肤癌是全球最常见的恶性肿瘤之一，早期检测对提升生存率和降低治疗成本至关重要。传统皮肤镜和视觉成像技术主要依赖可见光谱，常无法捕捉早期病变的细微光谱特征。本研究提出一种创新框架，结合多光谱超表面成像与基于卷积神经网络（CNN）和视觉变换器（ViT）的混合深度学习架构。设计的超表面可非侵入式获取对组织变化高度敏感的丰富光谱信息，而混合CNN-ViT模型能同步提取局部与全局特征，实现对皮肤病变的鲁棒分类。仿真评估显示，该方法准确率达约98%，敏感度95%，特异度99%，优于传统的RGB成像及单一架构方法。通过注意力图的定性分析发现，模型聚焦于临床相关病变区域，提升了可解释性。整体表明，结合超表面多光谱成像与混合深度学习，可推动新一代皮肤科诊断工具的发展，并为便携、快速、高精度的临床系统铺平道路。
原文摘要 · Abstract (English)
Skin cancer is among the most prevalent malignancies worldwiAdbe satnradcitts early detection is essential for improving patient survival and reducing treatment costs Conventional dermoscopic and visual imaging techniques are primarily limited to the visible spectrum and often fail to capture subtle spectral signatures associated with early stage malignancies This study proposes an innovative framework that integrates a multispectral metasurface for imaging with a hybrid deep learning architecture based on Convolutional Neural Networks and Vision Transformers The designed metasurface enables noninvasive acquisition of rich spectral information highly sensitive to tissue alterations while the hybrid CNN ViT model simultaneously extracts local and global features to robustly classify skin lesions Simulation-based evaluations demonstrate that the proposed method achieves approximately 98 accuracy 95 percentages sensitivity and 99 perentage specificity surpassing conventional RGB-based and single-architecture approaches Qualitative analyses using attention maps reveal that the model focuses on clinically relevant lesion regions improving interpretability Overall the results indicate that combining metasurface based multispectral imaging with hybrid deep learning can introduce a new generation of diagnostic tools in dermatology and pave the way for portable fast and highly accurate clinical systems
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