Gupta Anunay, Gupta Shreyansh, Katarya Rahul
Department of Electrical Engineering, Delhi Technological University, New Delhi, India.
Department of Computer Science, Delhi Technological University, New Delhi, India.
Appl Soft Comput. 2021 Feb;99:106859. doi: 10.1016/j.asoc.2020.106859. Epub 2020 Oct 29.
Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.
最近，全球都感染了新发现的冠状病毒（COVID-19）。严重急性呼吸综合征冠状病毒2（SARS-CoV-2），即广为人知的COVID-19，已被证明是一种危害极大的病毒，严重影响人们的健康。它会引发呼吸道疾病，尤其是在那些已经患有其他疾病的人群中。检测试剂盒的供应有限，以及其症状与肺炎等其他疾病相似，使得这种疾病具有致命性，已导致数百万人死亡。人们发现人工智能模型在生物医学领域对各种疾病的诊断中非常成功。在本文中，提出了一种集成的堆叠深度卷积网络InstaCovNet-19。所提出的模型利用了各种预训练模型，如ResNet101、Xception、InceptionV3、MobileNet和NASNet，以弥补训练数据量相对较少的问题。所提出的模型通过识别受感染人员胸部X光图像中由这些疾病引起的异常来检测COVID-19和肺炎。所提出的模型在3类（COVID-19、肺炎、正常）分类上的准确率达到99.08%，而在2类（COVID、非COVID）分类上的准确率达到99.53%。所提出的模型在三元分类上的平均召回率、F1分数和精确率分别为99%、99%和99%，而在二元分类中，COVID类别的精确率达到100%，召回率达到99%。InstaCovNet-19能够在无需任何人工干预的情况下，以经济的成本高精度地检测COVID-19，在这个隔离时代将极大地造福人类。
