Albataineh Zaid, Aldrweesh Fatima, Alzubaidi Mohammad A
Department of Electronic Engineering, Yarmouk University, Irbid, 21163 Jordan.
Department of Computer Engineering, Yarmouk University, Irbid, 21163 Jordan.
Cluster Comput. 2023 Jan 24:1-16. doi: 10.1007/s10586-023-03972-5.
As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.
作为一种大流行病，冠状病毒病（COVID-19）的主要评估工具仍存在严重缺陷。为改善现有状况，应利用该领域所有可用的设施和工具来抗击这一流行病。逆转录聚合酶链反应用于评估一个人是否感染了这种病毒，但它无法确定疾病的严重程度。在本文中，我们提出了一种简单、可靠且自动的系统，基于简单的分割方法以及从CT图像中提取的三种特征（感染比例、统计纹理特征（均值、标准差、偏度和峰度）、灰度共生矩阵（GLCM）和灰度游程长度矩阵（GLRLM）纹理特征），将COVID-19的CT扫描图像诊断为三个严重程度阶段：轻度、中度和重度。使用四种机器学习技术（决策树（DT）、K近邻（KNN）、支持向量机（SVM）和朴素贝叶斯）对扫描图像进行分类。根据扫描中的CT结果以及与数据集一起找到的描述文件，将1801张扫描图像分为四个阶段。我们提出的模型分为四个步骤：预处理、特征提取、分类和性能评估。在分类步骤中使用了四种机器学习算法：SVM、KNN、DT和朴素贝叶斯。通过SVM方法，所提出的模型在正常、轻度、中度和重度阶段的COVID-19感染分割准确率分别达到99.12%、98.24%、98.73%和99.9%。该模型的曲线下面积为0.99。最后，我们提出的模型比现有模型具有更好的性能。这将有助于医生了解感染阶段，从而缩短时间并为该阶段给予适当的治疗剂量。
