Department of Electrical, Computer and Biomedical Engineering, University of Pavia, I-27100 Pavia, Italy.
Sensors (Basel). 2024 Feb 21;24(5):1399. doi: 10.3390/s24051399.
Hyperspectral imaging (HSI) has become a very compelling technique in different scientific areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics, and medicine. In the latter, HSI plays a crucial role as a diagnostic support and for surgery guidance. However, the computational effort in elaborating hyperspectral data is not trivial. Furthermore, the demand for detecting diseases in a short time is undeniable. In this paper, we take up this challenge by parallelizing three machine-learning methods among those that are the most intensively used: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) algorithms using the Compute Unified Device Architecture (CUDA) to accelerate the classification of hyperspectral skin cancer images. They all showed a good performance in HS image classification, in particular when the size of the dataset is limited, as demonstrated in the literature. We illustrate the parallelization techniques adopted for each approach, highlighting the suitability of Graphical Processing Units (GPUs) to this aim. Experimental results show that parallel SVM and XGB algorithms significantly improve the classification times in comparison with their serial counterparts.
高光谱成像（HSI）在不同的科学领域已经成为一种非常有吸引力的技术；事实上，许多研究人员在遥感、农业、法医学和医学领域使用它。在后者中，HSI 作为一种诊断支持和手术指导起着至关重要的作用。然而，处理高光谱数据的计算工作量并不简单。此外，在短时间内检测疾病的需求是不可否认的。在本文中，我们通过使用 Compute Unified Device Architecture (CUDA) 并行化三种在最广泛使用的机器学习方法中：支持向量机 (SVM)、随机森林 (RF) 和极端梯度提升 (XGB) 算法，来应对这一挑战，以加速高光谱皮肤癌图像的分类。正如文献中所证明的那样，它们在 HS 图像分类中都表现出了良好的性能，特别是在数据集规模有限的情况下。我们说明了为每种方法采用的并行化技术，突出了图形处理单元 (GPU) 对此目的的适用性。实验结果表明，与串行算法相比，并行 SVM 和 XGB 算法显著提高了分类时间。
