Sharma Anubhav, Singh Karamjeet, Koundal Deepika
Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.
Department of Virtualization, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, Uttrakhand, India.
Biomed Signal Process Control. 2022 Aug;77:103778. doi: 10.1016/j.bspc.2022.103778. Epub 2022 May 2.
Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic.
冠状病毒病是一种由新型冠状病毒（CoV）引起的病毒感染，该病毒于2019年12月初在中国武汉市首次被发现。它通过引起呼吸道感染来影响人体呼吸系统，症状从轻微到严重，如发热、咳嗽和乏力，但可能进一步导致其他严重疾病，截至目前已造成数百万人死亡。因此，对于当前的医疗系统而言，准确诊断此类疾病非常必要。本文描述了一种先进的深度学习方法。我们提出了COVDC-Net，一种基于深度卷积网络的分类方法，它能够从胸部X光图像中识别出健康人和/或肺炎患者中感染了SARS-CoV-2的人。该方法使用了两个在ImageNet上预训练的经过修改的模型，即MobileNetV2和VGG16，但去掉了它们的分类器层，并使用置信度融合方法将这两个模型融合，以在两个当前公开可用的数据集上获得更好的分类准确率。通过详尽的实验观察到，对于3类（新冠病毒病、正常和肺炎）分类任务，所提出的方法实现了96.48%的总体分类准确率。对于4类分类（新冠病毒病、正常、病毒性肺炎和细菌性肺炎），COVDC-Net方法的准确率为90.22%。实验结果表明，与当前新冠疫情中针对同一任务提出的现有深度学习方法相比，所提出的COVDC-Net方法显示出了更好的总体分类准确率。
