结合深层密集聚合的新冠肺炎CT图像分类方法

COVID-19 CT image classification method combined with deep layer dense aggregation

新型冠状病毒肺炎在全球范围迅速蔓延,为快速准确地对其诊断,进而阻断疫情传播链,提出一种基于深度学习的分类网络DLDA-A-DenseNet。首先将深层密集聚合结构与DenseNet-201结合,对不同阶段的特征信息聚合,以加强对病灶的识别及定位能力;其次提出高效多尺度长程注意力以细化聚合的特征;此外针对CT图像数据集类别不均衡问题,使用均衡抽样训练策略消除偏向性。在中国胸部CT图像调查研究会提供的数据集上测试,所提方法较原始DenseNet-201在准确率、召回率、精确率、F1分数和Kappa系数提高了2.24%、3.09%、2.09%、2.60%和3.48%;并在COVID-CISet图像数据集上测试,取得99.50%的最优准确率。结果表明,对比其他方法,提出的新冠肺炎CT图像分类方法充分提取了CT切片的病灶特征,具有更高的精度和良好的泛化性。

COVID-19 is spreading rapidly around the world.In order to diagnose it quickly and accurately and thus block the chain of epidemic transmission,the study proposed a deep learning-based classification network DLDA-A-DenseNet.Firstly,DenseNet-201 combined deep layer dense aggregation to aggregate feature information at different stages to enhance its ability to identify and localize lesions.Secondly,this paper proposed efficient multi-scale long-range attention to refine the aggregated features.Moreover,this paper used a balanced sampling training strategy to eliminate the bias for the class imbalance problem of CT image dataset.Testing on the China consortium of chest CT image investigation dataset,the method improved 2.24%,3.09%,2.09%,2.60%and 3.48%in accuracy,recall,precision,F 1 score and Kappa coefficient compared with DenseNet-201,and achieved an optimal accuracy of 99.50%on COVID-CISet image dataset.The results show that the proposed COVID-19 CT image classification method can fully extract the lesion features of CT slices compared with other methods,and has higher classification accuracy and good generalizability.