Purpose: The main purpose in this study is to develop a pipeline for COVID-19 detection from a big and challenging database of Computed Tomography (CT) images. The proposed pipeline includes a segmentation part, a lung extraction part, and a classifier part. Methods: The methodologies tried in the segmentation part are traditional segmentation methods as well as UNet-based methods. In the classification part, a Convolutional Neural Network (CNN) was used to take the final diagnosis decisions. Results: In the segmentation part, the proposed segmentation methods show high dice scores on a publicly available dataset. In the classification part, the results were compared at slice-level and at patient-level as well. At slice-level, methods were compared and showed high validation accuracy indicating efficiency in predicting 2D slices. At patient level, the proposed methods were also compared in terms of validation accuracy and macro F1 score on the validation set. The dataset used for classification is COV-19CT Database. The method proposed here showed improvement from our precious results on the same dataset. Conclusion: The improved work in this paper has potential clinical usages for COVID-19 detection and diagnosis via CT images. The code is on github at https://github.com/IDU-CVLab/COV19D_3rd

翻译：目的：本研究的主要目的是开发一种流水线，用于从大规模且具有挑战性的计算机断层扫描（CT）图像数据库中检测COVID-19。所提出的流水线包括分割模块、肺部提取模块和分类器模块。方法：在分割模块中，尝试了传统分割方法以及基于UNet的方法。在分类模块中，使用卷积神经网络（CNN）做出最终诊断决策。结果：在分割模块中，所提出的分割方法在公开数据集上表现出较高的Dice系数。在分类模块中，分别在切片层面和患者层面进行了结果比较。在切片层面，各方法进行了比较并显示出较高的验证准确率，表明其在预测二维切片方面的有效性。在患者层面，所提出的方法也在验证集上就验证准确率和宏F1分数进行了比较。用于分类的数据集是COV-19CT数据库。本文提出的方法相比我们先前在该数据集上的结果有所改进。结论：本文的改进工作对通过CT图像进行COVID-19检测与诊断具有潜在的临床应用价值。代码托管于GitHub：https://github.com/IDU-CVLab/COV19D_3rd