Purpose The main purpose in this study is to propose 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 region of interest extraction part, and a classifier part. Methods The methodology used in the segmentation part is traditional segmentation methods as well as UNet based segmentation. 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 vailable dataset. In the classification part, the results show high accuracy on the validation partition of COV19-CT-DB dataset as well as higher precision, recall, and macro F1 score. The classification results were compared to our previous works other studies as well as on the same dataset. Conclusions The improved work in this paper proposes efficient pipeline with a potential of having clinical usage 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建议从一个具有挑战性的大型计算机成像(CT)图像数据库中探测COVID-19的管道。拟议的管道包括一个分割部分、一个有利益提取部分的区域和一个分类部分。分割部分使用的方法是传统分割方法和基于UNet的分割部分。在分类部分中,使用了一个革命神经网络(CNN)来作出最后诊断决定。在分块部分中,拟议的分割方法显示在公开可航行数据集中得分很高。在分类部分中,结果显示COV19-CTDB数据集的验证分布高度精准,以及更高的精确度、回顾和宏观F1评分。分类结果与我们以前的其他研究以及同一数据集进行比较。结论:本文件改进后的工作提出了高效的管道,有可能通过CT图像进行COVID-19的临床检测和诊断。代码在 https://github.com/IDU-CVLA/COV19_DD_DRVD_D。