Shahin Osama R, Alshammari Hamoud H, Taloba Ahmed I, El-Aziz Rasha M Abd
Department of Computer Science, College of Science and Arts in Gurayat, Jouf University, SaudiArabia.
Information Systems Department, College of Computer and information sciences, Sakaka, Jouf University, Saudi Arabia.
Comput Electr Eng. 2022 Jul;101:108055. doi: 10.1016/j.compeleceng.2022.108055. Epub 2022 Apr 29.
As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify corona virus using machine learning. To spot and identify corona virus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing corona virus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, the use of detected, infected areas that obtained in the detection phase with a scale of 50×50 and perform segmentation of the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here a support vector machine (SVM) and Radial basis function (RBF) has been utilized. Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.
由于全世界的人都容易受到新冠病毒的影响，因此对这种病毒的自动检测是一个重要问题。本文旨在利用机器学习检测和分类冠状病毒。在CT肺部筛查和计算机辅助诊断（CAD）系统中发现和识别冠状病毒，预计可区分和分类新冠病毒。借助决策树、支持向量机、K均值聚类和径向基函数等一些机器学习技术，利用从新冠病毒感染患者身上获取的临床标本。虽然一些专家认为RT-PCR检测是诊断新冠患者的最佳选择，但另一些人认为肺部CT扫描在诊断冠状病毒感染方面可能更准确，而且比PCR检测成本更低。临床标本包括血清标本、呼吸道分泌物和全血标本。总体而言，根据之前的临床检查结果，从这些标本中测量了15个因素。所提出的CAD系统包括四个阶段，首先是CT肺部筛查收集，然后是预处理阶段，以增强磨玻璃影（GGO）结节的外观，因为它们最初看起来模糊且对比度较低。将使用改进的K均值算法来检测和分割这些区域。最后，使用在检测阶段获得的50×50大小的检测到的感染区域，并对看似为GGO的实性假阳性进行分割，作为机器学习分类器（这里使用了支持向量机（SVM）和径向基函数（RBF））的输入和目标。此外，还开发了一个GUI应用程序，通过给出从临床标本中获得的15个输入因素，避免医生在获取准确结果时产生混淆。
