Dina Kushenchirekova, Andrey Kurenkov, D. Mamyrov, D. Viderman, Seong-Jun Lee, Min-Ho Lee
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引用次数: 4
摘要
新冠肺炎疫情是对全社会的全球性挑战,同时也为科学发展、科学传播和科学信息开放创造了独特的局面。2019年初,世界面临新冠肺炎大流行。冠状病毒极大地影响了全球大多数人的生活。深度学习方法可以从肺部的计算机断层扫描(CT)中自动分类冠状病毒疾病。在我们的工作中,我们测试了几种流行的卷积神经网络(CNN)模型来对CT扫描的切片进行分类。在本研究中,我们发现VGG-19模型在DenseNet201、MobileNetV2、Xception、VGG-16和ResNet50v2等其他测试模型中具有最好的分类精度。特别是,该模型对covid - x CT数据集的准确率为99.08%,对SARS-CoV-2 CT数据集的准确率为98.44%,对UCSD COVID-CT数据集的准确率为92.30%。此外,我们的结果包括3D热图,解释了每个模型的分类结果,显示了受冠状病毒影响的肺部区域。
COVID-19 classification based on CNNs models in CT image datasets
The COVID-19 coronavirus pandemic was a global challenge to the whole society and at the same time created a unique situation for the development of science, scientific communication and open access to scientific information. At the beginning of 2019 the world has faced a pandemic of Covid-19 coronavirus. The coronavirus impacted dramatically lives of majority people around the globe. Deep learning methods allow automatic classification of the coronavirus disease from the computer tomography (CT) scans of the lung. In our work we test several popular convolutional neural network (CNN) models to classify slices of the CT scans. In this study we indicate that the VGG-19 model gives the best classification accuracy among the other tested models such as DenseNet201, MobileNetV2, Xception, VGG-16 and ResNet50v2. In particular, the model achieves the accuracy of 99.08% for CovidX CT Dataset and 98.44% for SARS-CoV-2 CT dataset and 92.30% for UCSD COVID-CT dataset. Additionally, our results include 3D heatmaps that explain classification results for each individual model, showing regions of the lung affected by the coronavirus.