Asif Sohaib, Wenhui Yi, Amjad Kamran, Jin Hou, Tao Yi, Jinhai Si
Key Laboratory for Information Photonic Technology of Shaanxi Province and Key Laboratory for Physical Electronics and Devices of the Ministry of Education, School of Electronic Science and Engineering, Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi China.
School of Computer Science and Engineering Central South University Changsha China.
Expert Syst. 2022 Jul 29. doi: 10.1111/exsy.13099.
Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of 1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.
冠状病毒病（COVID-19）是一场已造成全球数千人伤亡和影响的大流行病。由于病例数量每日增加，大多数国家的医院都面临COVID-19检测试剂盒短缺的问题。早期检测COVID-19可以保护人们免受严重感染。不幸的是，COVID-19可能被误诊为肺炎或其他疾病，并可能导致患者死亡。因此，为了避免COVID-19在人群中传播，有必要实施一个自动化早期诊断系统作为快速替代诊断系统。几位研究人员在检测COVID-19方面做得很好；然而，他们中的大多数存在准确率较低和过拟合问题，这使得COVID-19的早期筛查变得困难。迁移学习是解决这个问题且准确率更高的最成功技术。在本文中，我们研究了应用迁移学习的可行性，并添加了我们自己的分类器来自动对COVID-19进行分类，因为由于数据可用性有限，迁移学习非常适合医学成像。在这项工作中，我们提出了一种基于深度迁移学习技术的卷积神经网络（CNN）模型，使用六种不同的预训练架构，包括VGG16、DenseNet201、MobileNetV2、ResNet50、Xception和EfficientNetB0。总共3886张胸部X光片（1200例COVID-19病例、1341例健康者和1345例病毒性肺炎病例）被用于研究所提出的CNN模型的有效性。为了找到最合适的模型，对使用三类胸部X光数据集的所提出的CNN模型进行了对比分析。实验结果表明，所提出的基于VGG16的CNN模型能够以97.84%的准确率、97.90%的精确率、97.89%的灵敏度和97.89%的F1分数准确诊断COVID-19患者。对测试数据的评估表明，所提出的模型在CNN中产生了最高的准确率，似乎是COVID-19分类的最合适选择。我们相信，在这种大流行情况下，这个模型将支持医疗保健专业人员改善患者筛查。
