基于改进Inception-v3网络的肺炎检测方法

Pneumonia detection method based on improved Inception-v3 network

在Inception-v3网络的基础上提出了一种有效的改进方法以提升X光胸片肺炎诊断的准确度.在原始Inception-v3网络结构中引入了残差连接以减轻网络加深带来的梯度消失问题.使用空间注意力与通道注意力机制提升模型的特征提取能力,同时通过通道混洗(Channel Shuffle)促进不同通道特征图之间的信息融合.最终改进模型的分类准确率为94.64%,较原始Inception-v3网络提升了4.33%,且对于新型冠状病毒感染具有更高的召回率,达到99.72%.实验结果表明,引入注意力机制的Inception-v3网络在四类肺炎检测分类任务(正常、普通病毒性肺炎、新型冠状病毒感染、其他肺部感染)中具有更高泛化能力与鲁棒性.

Based on the Perception-v3 network,this paper proposes an effective method to improve the accuracy of X-ray chest diagnosis of pneumonia.The introduction of spatial attention and channel attention mechanism in the original Inception-v3 network structure improves the feature extraction ability of the model,and promotes the information fusion between different channel feature maps through channel shuffle.The classification accuracy of the improved model is 94.6%,which is 4.3%higher than that of the original Inception-v3 network and has a higher recall rate for COVID-19,reaching 99.72%.The experimental results show that Inception-v3 network with attention mechanism has higher generalization ability and robustness in four types of pneumonia detection and classification tasks(normal,common viral pneumonia,COVID-19,other lung infections).