The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

中文翻译：

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社会群体优化辅助 Kapur 的熵和形态分割，用于从计算机断层扫描图像中自动检测 COVID-19 感染。


由新型冠状病毒 SARS-CoV-2 引起的冠状病毒病 (COVID-19) 已被宣布为全球大流行。由于其感染率和严重性，它已成为当代主要的全球威胁之一。为了支持当前对抗这种疾病的斗争，本研究旨在提出一种基于机器学习的管道，使用肺部计算机断层扫描图像 (CTI) 检测 COVID-19 感染。该实施的管道由许多子程序组成，从分割 COVID-19 感染到对分割区域进行分类。该管道的初始部分使用基于社会群体优化的 Kapur 熵阈值对受 COVID-19 影响的 CTI 进行分割，然后进行 k 均值聚类和基于形态的分割。管道的下一部分实现特征提取、选择和融合以对感染进行分类。基于主成分分析的串行融合技术用于融合特征，然后使用融合的特征向量来训练、测试和验证四种不同的分类器，即随机森林、K 最近邻 (KNN)、径向基支持向量机函数和决策树。使用基准数据集的实验结果表明基于形态学的分割任务具有较高的准确度（> 91%）；对于分类任务，KNN 在比较的分类器中提供了最高的准确度 (> 87%)。但值得注意的是，该方法仍有待临床验证，因此不应用于临床诊断正在进行的 COVID-19 感染。