新冠肺炎CXR图像分类新模型COVID-SERA-NeXt

A New Classification Model COVID-SERA-NeXt for COVID-19 CXR Images

新型冠状病毒(COVID-19)感染者胸部X射线(Chest X-ray,CXR)图像不同于正常人,是诊断的有效依据。在ResNeXt模型基础上,加入交叉堆叠的通道注意力模块和残差注意力模块以及提出的维度降解模块,提出了针对COVID-19 CXR图像分类的COVID-SERA-NeXt模型。对公开访问的基准数据集COVIDx进行图像分类,实验结果显示,提出的COVID-SERA-NeXt模型在多项指标上优于其基础模型ResNeXt,其中准确率、宏召回率分别提高到96.11%、95.46%.经过ChestX-ray8医学图像预训练的COVID-SERA-NeXt模型对COVIDx数据集的分类性能更进一步提升。

The Chest X-ray(CXR)images of COVID-19 patients are different from those of normal people,which has been an effective base for making correct diagnosis.It is an important way to help medicine doctors to make the fast and accurate diagnosis for patients by using computer aided automatic classification technique based on the patient chest X-ray images.The new COVID-SERA-NeXt model was proposed in this paper for classifying COVID-19 CXR images by introducing the cross-stacked channel attention module and residual attention module,as well as the proposed dimensional reduction module,into the ResNeXt model.The performance of the proposed COVID-SERA-NeXt model was tested on the open accessed COVIDx dataset by extensive experiments.The experimental results show that the proposed COVID-SERA-NeXt model is superior to its base model ResNeXt.It achieves the accuracy and Macro_Recall of 96.11%and 95.46%,respectively.Further experiments demonstrate that the proposed COVID-SERA-NeXt model achieves better performance to classify COVID-19 CXR images when it is pre-trained using ChestX-ray8 dataset.