Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

自 COVID-19 出现以来，成千上万的人每天都接受胸部 X 光检查和计算机断层扫描以进行筛查。这增加了放射科医生的工作量，许多病例积压。这不仅适用于 COVID-19，也适用于其他需要放射诊断的异常情况。在这项工作中，我们提出了一种在计算机断层扫描图像上快速诊断 COVID-19 的自动化技术。所提出的技术包括四个主要步骤：（1）数据收集和归一化，（2）相关特征的提取，（3）最佳特征的选择和（4）特征分类。在数据收集步骤中，我们从公共域网站收集多个患者的数据，并执行预处理，包括图像大小调整。在接下来的步骤中，我们应用离散小波变换和基于扩展分割的分形纹理分析方法来提取相关特征。随后应用熵控制的遗传算法从每种特征类型中选择最佳特征，这些特征使用串行方法进行组合。在最后阶段，对最佳特征进行各种分类器进行诊断。所提出的框架，当增加朴素贝叶斯分类器时，产生了 92.6% 的最佳准确度。模拟结果得到详细统计分析的支持，作为概念证明。随后应用熵控制的遗传算法从每种特征类型中选择最佳特征，这些特征使用串行方法进行组合。在最后阶段，对最佳特征进行各种分类器进行诊断。所提出的框架，当增加朴素贝叶斯分类器时，产生了 92.6% 的最佳准确度。模拟结果得到详细统计分析的支持，作为概念证明。随后应用熵控制的遗传算法从每种特征类型中选择最佳特征，这些特征使用串行方法进行组合。在最后阶段，对最佳特征进行各种分类器进行诊断。所提出的框架，当增加朴素贝叶斯分类器时，产生了 92.6% 的最佳准确度。模拟结果得到详细统计分析的支持，作为概念证明。