The main purpose of 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. Optional slice removal techniques after UNet-based segmentation of slices were also tried. 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. In terms of the 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. In 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：本研究的主要目的是开发一种流水线，用于从规模庞大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模块。还尝试了在基于UNet的切片分割后进行可选的切片移除技术。分割部分尝试的方法包括传统分割方法以及基于UNet的方法。在分类部分，使用了卷积神经网络（CNN）进行最终诊断决策。从结果来看：在分割部分，所提出的分割方法在公开数据集上展示了较高的Dice得分。在分类部分，结果在切片层面和患者层面进行了比较。在切片层面，方法进行了比较并显示出较高的验证准确率，表明在预测二维切片方面具有高效性。在患者层面，所提出的方法也在验证准确率和验证集上的宏F1分数方面进行了比较。分类所用的数据集为COV-19CT数据库。本文提出的方法在相同数据集上相较于我们之前的结果有所改进。结论是，本文的改进工作对通过CT图像进行COVID-19检测和诊断具有潜在的临床用途。相关代码已在GitHub上发布，网址为https://github.com/IDU-CVLab/COV19D_3rd。