A deep learning model for mass screening of COVID‐19,International Journal of Imaging Systems and Technology

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A deep learning model for mass screening of COVID‐19
International Journal of Imaging Systems and Technology ( IF 3.0 ) Pub Date : 2021-02-03 , DOI: 10.1002/ima.22544
Vijaypal Singh Dhaka ₁ , Geeta Rani ₁ , Meet Ganpatlal Oza ₁ , Tarushi Sharma ₁ , Ankit Misra ₂

Affiliation

The objective of this research is to develop a convolutional neural network model ‘COVID‐Screen‐Net’ for multi‐class classification of chest X‐ray images into three classes viz. COVID‐19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X‐ray images and accurately identifies the features responsible for distinguishing the X‐ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine‐tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X‐ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the ‘COVID‐Screen‐Net’ outperforms the existing systems for screening of COVID‐19. The effectiveness of the model is validated by the radiology experts on the real‐time dataset. Therefore, it may prove a useful tool for quick and low‐cost mass screening of patients of COVID‐19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number ‘SW‐13625/2020’.

中文翻译：

用于大规模筛查 COVID-19 的深度学习模型

本研究的目的是开发一种卷积神经网络模型“COVID-Screen-Net”，用于将胸部 X 射线图像多类分类为三类，即。COVID-19、细菌性肺炎和正常。该模型对 X 射线图像进行自动特征提取，并准确识别负责区分不同类别 X 射线图像的特征。它在 GradCam 上绘制这些特征。作者根据数据集的大小优化了卷积层和激活层的数量。他们还微调了超参数以最小化计算时间并提高模型的效率。该模型的性能已经在从医院收集的匿名胸部 X 光图像和网络上可用的数据集上进行了评估。该模型的平均准确率为 97。71%，最大召回率为 100%。比较分析表明，“COVID-Screen-Net”优于现有的 COVID-19 筛查系统。放射学专家在实时数据集上验证了模型的有效性。因此，它可能被证明是对 COVID-19 患者进行快速和低成本大规模筛查的有用工具。在当前全球大流行的情况下，该工具可能会减轻卫生专家的负担。该工具的版权根据印度知识产权法以作者名义注册，注册号为“SW-13625/2020”。因此，它可能被证明是对 COVID-19 患者进行快速和低成本大规模筛查的有用工具。在当前全球大流行的情况下，该工具可能会减轻卫生专家的负担。该工具的版权根据印度知识产权法以作者名义注册，注册号为“SW-13625/2020”。因此，它可能被证明是对 COVID-19 患者进行快速和低成本大规模筛查的有用工具。在当前全球大流行的情况下，该工具可能会减轻卫生专家的负担。该工具的版权根据印度知识产权法以作者名义注册，注册号为“SW-13625/2020”。

更新日期：2021-02-03

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