Islam Md Robiul, Nahiduzzaman Md
Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.
Expert Syst Appl. 2022 Jun 1;195:116554. doi: 10.1016/j.eswa.2022.116554. Epub 2022 Feb 4.
Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively.
最近，最具传染性的疾病是新型冠状病毒病（COVID - 19），它对全球200多个国家的公共卫生造成了毁灭性影响。由于使用逆转录聚合酶链反应（RT - PCR）检测COVID - 19既耗时又容易出错，检测的替代解决方案是计算机断层扫描（CT）图像。在本文中，对比度受限直方图均衡化（CLAHE）被应用于CT图像作为预处理步骤，以提高图像质量。之后，我们开发了一种新颖的卷积神经网络（CNN）模型，该模型从总共2482张CT扫描图像中提取了100个显著特征。然后将这些提取的特征应用于各种机器学习算法——高斯朴素贝叶斯（GNB）、支持向量机（SVM）、决策树（DT）、逻辑回归（LR）和随机森林（RF）。最后，我们提出了一种用于COVID - 19 CT图像分类的集成模型。我们还展示了与现有方法的各种性能比较。我们提出的模型优于现有模型，准确率、精确率和召回率分别达到了99.73%、99.46%和100%。
