Abstract

BACKGROUND:

Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time.

OBJECTIVE:

This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET.

METHODS:

To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model.

RESULTS:

Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility.

CONCLUSION:

This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

中文翻译：

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使用转移学习从胸部X射线图像诊断COVID-19：通过消除数据加载器的偏斜来提高性能

摘要

背景：

胸部X射线成像易于获取，成本低，成像时间短，已被证明是检测和诊断COVID-19病例的有力诊断方法。

目的：

这项研究旨在借助名为nCoV-NET的深层卷积神经网络模型（CNN）来提高使用X线胸片筛查被COVID-19感染的患者的效率。

方法：

为了训练和评估所开发模型的性能，分别从“ ChestX-ray14”，“ COVID-19图像数据收集”和“印第安纳大学的Chest X射线收集”资源中收集了三个数据集。总体而言，本研究涉及299例COVID-19肺炎病例和1,522例非COVID 19肺炎病例。为了克服由于两类数据集中的案例不平衡而造成的可能偏差，在过程的微调阶段对ResNet，DenseNet和VGG架构进行了重新训练，以使用转移学习方法区分COVID-19类。最后，将优化的最终nCoV-NET模型应用于测试数据集，以验证所提出模型的性能。

结果：

尽管所有经过重新训练的体系结构的性能参数都彼此接近，但最终的nCOV-NET模型在迁移学习阶段使用DenseNet-161体系结构进行了优化，在COVID-19案例分类中表现出最高的性能，其准确性为97.1％。激活图方法用于创建激活图，突出显示射线照相的关键区域以改善因果关系和清晰度。

结论：

这项研究表明，提出的称为nCoV-NET的CNN模型可以利用胸部X射线图像可靠地检测出COVID-19病例，从而加快分诊和节省疾病控制的关键时间，并协助放射科医生验证其初步诊断。