Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9676, 0.9181, 0.9456, 0.9545, and 0.9890, respectively; using pipeline approaches, the values were 0.9832, 0.9622, 0.9577, 0.9642, and 0.9923, respectively. For COVID-19 pneumonia/other pneumonia classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9615, 0.7270, 0.8846, 0.9180, and 0.9370, respectively; using pipeline approaches, the values were 0.9915, 0.8140, 0.9071, 0.9327, and 0.9615, respectively. The results of this study show that classification success can be increased by reducing the time to obtain per-image results through using the proposed pipeline approaches.

感染 COVID-19 病毒的患者会出现严重肺炎，通常会导致死亡。放射学证据表明，该疾病会导致肺间质受累和肺部混浊，以及双侧毛玻璃样混浊和斑片状混浊。在本研究中，提出了新的流程建议，并对其性能进行了测试，以减少 COVID-19 诊断中的假阴性 (FN)、假阳性 (FP) 和错误分类图像总数 (FN + FP) （COVID-19/非 COVID-19 和 COVID-19 肺炎/其他肺炎）来自 CT 肺部图像。总共 4320 张 CT 肺部图像（其中 2554 张与 COVID-19 相关，1766 张与非 COVID-19 相关）用于 COVID-19 和非 COVID-19 分类的测试程序。同样，总共 3801 张 CT 肺部图像，其中 2554 张与 COVID-19 肺炎相关，1247 张与其他肺炎相关，用于 COVID-19 肺炎和其他肺炎分类的测试程序。 24 层卷积神经网络 (CNN) 架构用于分类过程。在本研究范围内，通过使用应用和不应用局部二值模式（LBP）的 CT 肺部图像获得两个实验的结果，并通过应用双树复小波变换（DT-CWT）获得子带图像到这些图像。接下来，使用本研究中提出的五种管道方法根据这两个结果计算新的分类结果。对于 COVID-19 和非 COVID-19 分类，在不使用管道方法的情况下获得的最高灵敏度、特异性、准确性、F-1 和 AUC 值分别为 0.9676、0.9181、0.9456、0.9545 和 0.9890；使用管道方法，值为 0.9832、0.9622、0.9577、0.9642 和 0。分别为9923。对于COVID-19肺炎/其他肺炎分类，不使用管道方法获得的最高敏感性、特异性、准确性、F-1和AUC值分别为0.9615、0.7270、0.8846、0.9180和0.9370；使用管道方法，值分别为 0.9915、0.8140、0.9071、0.9327 和 0.9615。本研究的结果表明，通过使用所提出的管道方法减少获得每幅图像结果的时间可以提高分类成功率。