论文标题
冠状病毒（COVID-19）使用机器学习方法的CT图像进行分类
Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods
论文作者
论文摘要
这项研究介绍了冠状病毒（Covid-19）的早期检测，该发现是由世界卫生组织（WHO）命名的，它是通过机器学习方法命名的。检测过程是在腹部计算机断层扫描（CT）图像上实现的。从CT图像中检测到的专家放射科医生与其他病毒性肺炎显示出不同的行为。因此，临床专家指出，需要在早期诊断COVİD-19病毒。为了检测COVID-19，从150个CT图像中取出大小为16x16、32x32、48x48、64x64，形成了四个不同的数据集。将特征提取过程应用于补丁以提高分类性能。灰度级别的共发生矩阵（GLCM），局部定向模式（LDP），灰度级别运行长度矩阵（GLRLM），灰级尺寸尺寸区域矩阵（GLSZM）和离散小波的变换（DWT）算法用作特征提取方法。支持向量机（SVM）分类了提取的功能。在分类过程中实施了2倍，5倍和10倍的交叉验证。灵敏度，特异性，准确性，精度和F得分度量指标用于评估分类性能。最佳分类精度为99.68％，使用10倍的交叉验证和GLSZM特征提取方法。
This study presents early phase detection of Coronavirus (COVID-19), which is named by World Health Organization (WHO), by machine learning methods. The detection process was implemented on abdominal Computed Tomography (CT) images. The expert radiologists detected from CT images that COVID-19 shows different behaviours from other viral pneumonia. Therefore, the clinical experts specify that COVİD-19 virus needs to be diagnosed in early phase. For detection of the COVID-19, four different datasets were formed by taking patches sized as 16x16, 32x32, 48x48, 64x64 from 150 CT images. The feature extraction process was applied to patches to increase the classification performance. Grey Level Co-occurrence Matrix (GLCM), Local Directional Pattern (LDP), Grey Level Run Length Matrix (GLRLM), Grey-Level Size Zone Matrix (GLSZM), and Discrete Wavelet Transform (DWT) algorithms were used as feature extraction methods. Support Vector Machines (SVM) classified the extracted features. 2-fold, 5-fold and 10-fold cross-validations were implemented during the classification process. Sensitivity, specificity, accuracy, precision, and F-score metrics were used to evaluate the classification performance. The best classification accuracy was obtained as 99.68% with 10-fold cross-validation and GLSZM feature extraction method.
