Abstract

BACKGROUND:

Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved.

OBJECTIVE:

To develop a new deep learning system of chest X-ray images and evaluate whether it can quickly and accurately detect pneumonia and COVID-19 patients.

METHODS:

The developed deep learning system (UBNet v3) uses three architectural hierarchies, namely first, to build an architecture containing 7 convolution layers and 3 ANN layers (UBNet v1) to classify between normal images and pneumonia images. Second, using 4 layers of convolution and 3 layers of ANN (UBNet v2) to classify between bacterial and viral pneumonia images. Third, using UBNet v1 to classify between pneumonia virus images and COVID-19 virus infected images. An open-source database with 9,250 chest X-ray images including 3,592 COVID-19 images were used in this study to train and test the developed deep learning models.

RESULTS:

CNN architecture with a hierarchical scheme developed in UBNet v3 using a simple architecture yielded following performance indices to detect chest X-ray images of COVID-19 patients namely, 99.6%accuracy, 99.7%precision, 99.7%sensitivity, 99.1%specificity, and F1 score of 99.74%. A desktop GUI-based monitoring and classification system supported by a simple CNN architecture can process each chest X-ray image to detect and classify COVID-19 image with an average time of 1.21 seconds.

CONCLUSION:

Using three hierarchical architectures in UBNet v3 improves system performance in classifying chest X-ray images of pneumonia and COVID-19 patients. A simple architecture also speeds up image processing time.

中文翻译：

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UBNet：基于深度学习的肺炎和 COVID-19 患者自动 X 射线图像检测方法

摘要

背景：

胸部 X 光图像分析是诊断 COVID-19 和肺炎患者的主要标准之一，比使用 PCR 拭子方法更快。然而，使用X射线图像的准确性需要提高。

客观的：

开发一种新的胸部 X 光图像深度学习系统，并评估它是否能够快速准确地检测肺炎和 COVID-19 患者。

方法：

开发的深度学习系统（UBNet v3）使用三个架构层次结构，即首先构建一个包含 7 个卷积层和 3 个 ANN 层的架构（UBNet v1），用于在正常图像和肺炎图像之间进行分类。其次，使用 4 层卷积和 3 层 ANN（UBNet v2）对细菌和病毒性肺炎图像进行分类。第三，使用 UBNet v1 对肺炎病毒图像和 COVID-19 病毒感染图像进行分类。本研究使用了一个包含 9,250 张胸部 X 光图像（包括 3,592 张 COVID-19 图像）的开源数据库来训练和测试开发的深度学习模型。

结果：

具有在 UBNet v3 中使用简单架构开发的分层方案的 CNN 架构产生以下性能指标来检测 COVID-19 患者的胸部 X 射线图像，即 99.6% 准确度、99.7% 精确度、99.7% 灵敏度、99.1% 特异性和 F1得分为 99.74%。由简单 CNN 架构支持的基于桌面 GUI 的监控和分类系统可以处理每张胸部 X 光图像以检测和分类 COVID-19 图像，平均时间为 1.21 秒。

结论：

在 UBNet v3 中使用三个分层架构可提高系统在分类​​肺炎和 COVID-19 患者的胸部 X 射线图像方面的性能。简单的架构还可以加快图像处理时间。