融合注意力机制和轻量级卷积神经网络的胸部CT影像分类方法研究

Research on Chest CT Image Classification Method Combining Attention Mechanism and Lightweight Convolutional Neural Network

同一疾病类型的CT影像也会由于患者患病严重程度不同而呈现差异,现主要临床诊断方法依赖医生专业能力及过往经验,客观性有待增强,效率有待提高。针对以上问题,提出一个融合注意力机制的CT分类网络—并联轻量级CT分类卷积神经网络(PC-CTNet)。该网络主要由并联支路通道混洗(PCS)模块和深度高效跳跃连接(DES)模块组成。PCS模块采用双分支并联,融合了多尺度感受野的特征;DES模块则利用卷积和高效通道注意力提取有效的深层类间区分信息,并通过跳跃连接避免梯度消失。结果表明,PC-CTNet模型在包含5988张大小不一的CT数据集上分类准确率能达到98.46%,在包含194922张的开源数据集上分类准确率能达到98.75%。PC-CTNet的各项性能指标均接近现有的胸部CT分类网络,且其参数量和计算量约为0.32、75.58 M,分别为实验比较中胸部CT分类网络的10.17%和3.21%,拥有更高的参数效率和计算效率,能有效辅助医生诊断,提高诊断效率和客观性。

CT images of the same disease type can also show differences due to the different severity of the patient′s disease.At present,main clinical diagnosis methods rely on personal ability and past experience of doctors,and the objectivity needs to be enhanced and the efficiency needs to be improved.In view of these problems,we proposed a CT classification network with attention mechanism-parallel lightweight convolutional neural network for CT classification(PC-CTNet).This network mainly consisted of parallel branch channel shuffle(PCS)module and deep-wise efficient shortcut connection(DES)module.PCS module adopted double branches,fused the features under the multi-scale receptive field.DES module used convolution and efficient channel attention to extract effective deep inter-class differentiation information,and alleviated gradient disappearance by shortcut connection.Experiments were conducted on two chest CT datasets,and the results showed that the classification accuracy of the PC-CTNet model reached 98.46%on the collected dataset with 5988 CT images in different sizes,and 98.75%on the open-source datasets with 194922 CT images.The performance indicators of PC-CTNet were close to the existing chest CT classification network,and its parameter and computational complexity was about 0.32 M and 75.58 M,respectively,which was 10.17%and 3.21%of the chest CT classification network in the experimental comparison.The proposed network has higher parameter and computational efficiency,can effectively assist doctors in diagnosis and improve diagnostic efficiency and objectivity.