论文标题
使用分割，区域提取和分类管道检测COVID-19
COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline
论文作者
论文摘要
这项研究的主要目的是从计算机断层扫描（CT）图像的大型且具有挑战性的数据库中开发用于COVID-19检测的管道。所提出的管道包括分割部分，肺提取部分和分类器部分。还尝试了基于UNET的切片切片后的可选切片去除技术。在分割部分中尝试的方法是传统的分割方法以及基于UNET的方法。在分类部分中，使用卷积神经网络（CNN）做出最终诊断决策。在结果方面：在分割部分中，提出的分割方法显示出公开可用的数据集上的骰子得分很高。在分类部分中，在切片级别和患者级别上比较结果。在切片级别，比较方法并显示出高验证精度，表明预测2D切片的效率。在患者级别上，还以验证精度和验证集中的宏F1分数进行比较。用于分类的数据集是COV-19CT数据库。这里提出的方法显示了我们在同一数据集上的宝贵结果的改善。总之，本文的改进工作具有通过CT图像进行COVID-19检测和诊断的潜在临床用法。该代码在https://github.com/idu-cvlab/cov19d_3rd上在github上
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
