Background:

The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients.

Aim:

The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients.

Method:

In this paper, an Ensemble Deep Convolution Neural Network model “CoVNet-19” is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic.

Results:

The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained.

Conclusion:

Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients.

中文翻译：

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CoVNet-19：用于检测和分析 COVID-19 患者的深度学习模型

背景：

与新冠病毒持续抗争，尽快得到治疗，迅速出具诊断报告，成为当务之急。每天有数百万人受到感染，死亡率为 2%，我们的动机是通过完成一种重要而实质性的诊断 COVID-19 患者的方法来为解决这个现实世界的问题做出一点贡献。

目的：

全球 COVID-19 病例的指数增长严重影响了人口稠密国家的医疗保健系统，因为医生、检测工具和其他资源的比例较少，因此成为识别感染者的必要条件。针对上述问题，本文旨在制定一种准确、高效、省时的检测阳性电晕患者的方法。

方法：

在本文中，提出了一种集成深度卷积神经网络模型“CoVNet-19”，它可以揭示重要的诊断特征，以使用胸部 X 光图像发现 COVID-19 感染患者，并帮助放射科医生和医学专家抗击这种流行病。

结果：

实验结果清楚地表明，所提出的 COVID-19、肺炎和正常三类分类方法的总体分类准确率为 98.28%，平均准确率和召回率分别为 98.33% 和 98.33%。除此之外，对于非 COVID 和 COVID 胸部 X 射线图像之间的二元分类，获得了 99.71% 的总体准确率。

结论：

我们提出的集成深度学习分类模型具有很高的诊断准确性，可以为检测 COVID-19 感染患者做出富有成效和实质性的贡献。