Hyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.

高光谱成像应用于医学领域，用于疾病特别是癌症的自动诊断。在各种分类算法中，最合适的是机器和深度学习技术。特别是，Vision Transformers 代表了一种创新的深层架构，可通过高光谱图像对皮肤癌进行分类。然而，此类方法计算量大，需要并行解决方案以确保快速分类。在本文中，利用边缘和云计算背景下的技术来评估并行视觉变压器，通过分析重要参数（如适用的处理时间、功耗和通信延迟）来设想便携式仪器的开发。使用了低功耗 GPU、不同型号的桌面 GPU 和用于科学计算的 GPU。云解决方案的处理时间较短，而基于 GPU 的边缘板具有最低的能耗，因此是便携式仪器的最佳选择，没有令人信服的时间限制。