Göreke Volkan, Sarı Vekil, Kockanat Serdar
Sivas Vocational School of Technical Sciences, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
Department of Electrical and Electronics Engineering, Sivas Cumhuriyet University, 58140, Sivas, Turkey.
Appl Soft Comput. 2021 Jul;106:107329. doi: 10.1016/j.asoc.2021.107329. Epub 2021 Mar 19.
Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. Along with causing many deaths, it has substantially affected the social life, economics, and infrastructure worldwide in a negative manner. Therefore, it is very important to be able to diagnose the COVID-19 quickly and correctly. In this study, a new feature group based on laboratory findings was obtained considering ethnical and genetic differences for interpretation of blood data. Then, using this feature group, a new hybrid classifier architecture based on deep learning was designed and COVID-19 detection was made. Classification performance indicators were obtained as accuracy of 94.95%, F1-score of 94.98%, precision of 94.98%, recall of 94.98% and AUC of 100%. Achieved results were compared with those of the deep learning classifiers suggested in literature. According to these results, proposed method shows superior performance and can provide more convenience and precision to experts for diagnosis of COVID-19 disease.
不幸的是，2019冠状病毒病（COVID-19）正在全球迅速蔓延。它在造成许多人死亡的同时，还对全球的社会生活、经济和基础设施产生了严重的负面影响。因此，能够快速、准确地诊断COVID-19非常重要。在本研究中，考虑到种族和基因差异以解释血液数据，基于实验室检查结果获得了一个新的特征组。然后，使用该特征组，设计了一种基于深度学习的新型混合分类器架构并进行了COVID-19检测。分类性能指标为：准确率94.95%、F1分数94.98%、精确率94.98%、召回率94.98%和曲线下面积100%。将取得的结果与文献中提出的深度学习分类器的结果进行了比较。根据这些结果，所提出的方法表现出优越的性能，可为专家诊断COVID-19疾病提供更多便利和精确性。
