Narin Ali, Kaya Ceren, Pamuk Ziynet
Department of Electrical and Electronics Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey.
Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, Zonguldak, 67100 Turkey.
Pattern Anal Appl. 2021;24(3):1207-1220. doi: 10.1007/s10044-021-00984-y. Epub 2021 May 9.
The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.
2019年新型冠状病毒病（COVID-19）起源于中国，已在其他国家的人群中迅速传播，根据世界卫生组织的统计，全球感染病例已接近1.01917147亿例。由于每日新增病例不断增加，医院中可用的COVID-19检测试剂盒数量有限。因此，有必要实施一种自动检测系统，作为一种快速的替代诊断选项，以防止COVID-19在人群中传播。在本研究中，提出了五种基于预训练卷积神经网络的模型（ResNet50、ResNet101、ResNet152、InceptionV3和Inception-ResNetV2），用于通过胸部X光片检测感染冠状病毒肺炎的患者。我们通过五折交叉验证实现了三种不同的二分类，共四个类别（COVID-19、正常（健康）、病毒性肺炎和细菌性肺炎）。考虑到所获得的性能结果，可以看出，在其他四个使用的模型中，预训练的ResNet50模型提供了最高的分类性能（数据集1的准确率为96.1%，数据集2的准确率为99.5%，数据集3的准确率为99.7%）。
