Chest X-ray emphysema detection algorithm based on channel attention and dilated convolution

Abstract

Abstract: Emphysema detection algorithm in chest X-ray plays an important role in clinical aided diagnosis. In order to solve the problems in existing algorithms, such as lacking feature channel screening ability, small receptive field of feature map, easy to be disturbed by local tissue noise, sample imbalance, EDACD (a chest X-ray emphysema detection algorithm based on channel attention and dilated convolution) is proposed in this paper. Firstly, the feature extraction network SE-ResNet and the feature pyramid network SE-FPN with channel selection ability are constructed by using channel attention module. At the same time, the dilated convolution is used to replace some common convolutions in SE-ResNet, and the robustness of the features is improved by increasing its receptive field. Finally, the focus loss is used as the classification loss function to make the network focus on training difficult samples. In addition, in the training process, the limited contrast adaptive histogram equalization algorithm is used to preprocess the image, which further highlights the characteristics of emphysema. Through data expansion and clustering labels to optimize the anchor parameters, so as to overcome the scarcity of labeled emphysema data and the inappropriate traditional anchors setting of emphysema. The subjective and objective experiment results in Chestx-Det10 and Chestx-Det14 datasets show that the proposed algorithm has better detection ability than the contrast algorithms.

Key words: emphysema detection, channel attention, dilated convolution, focal loss

CLC Number:

TP393

LI Ce, XU Da-you, JIN Shan-gang, GAO Wei-zhe, CHEN Xiao-lei. Chest X-ray emphysema detection algorithm based on channel attention and dilated convolution[J]. Journal of Lanzhou University of Technology, 2022, 48(2): 81-89.

References

[1] 施俊,汪琳琳,王珊珊,等.深度学习在医学影像中的应用综述 [J].中国图象图形学报,2020,25(10):1953-1981.
[2] CAO L,LI W J,FENG Q J.Automatic detection and diagnosis of lung nodules on CT images based on LDA and SVM [J].Journal of Southern Medical University,2011,31(2):324-328.
[3] REN J.ANN vs.SVM:Which one performs better in classification of MCCs in mammogram imaging [J].Knowledge-Based Systems,2012,26:144-153.
[4] WANG X,PENG Y,LU L,et al.Chestx-ray8:Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2017:2097-2106.
[5] YAO L,POBLENZ E,DAGUNTS D,et al.Learning to diagnose from scratch by exploiting dependencies among labels [J/OL].[2020-10-15].https://arxiv.org/pdf/1710.10501.pdf.
[6] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition [J].Computer Science,2014,1(20):332-342.
[7] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition [C/OL].[2020-10-15].https://arxiv.org/pdf/1512.03385.pdf.
[8] HU J,SHEN L,SUN G.Squeeze-and-excitation networks [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2018:7132-7141.
[9] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection [C]//Proceedings of the IEEE International Conference on Computer Vision.[S.l.]:IEEE,2017:2980-2988.
[10] PANG J,CHEN K,SHI J,et al.Libra R-CNN:Towards balanced learning for object detection [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2019:821-830.
[11] 李策,刘昊,陈晓雷,等.基于多感知特征计算的沙尘图像增强算法 [J].兰州理工大学学报,2018,44(4):90-95.
[12] REDMON J,FARHADI A.YOLO9000:better,faster,stronger [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[S.l.]:IEEE,2017:7263-7271.
[13] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[14] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single shot multibox detector [C/OL].[2020-10-15].https://arxiv.org/abs/1512.02325.
[15] ZHAO Q,SHENG T,WANG Y,et al.M2det:A single-shot object detector based on multi-level feature pyramid network [C/OL].[2020-10-15].https:arxiv.org/abs/1811.04533.
[16] LIU J,LIAN J,YU Y.ChestX-det10:chest X-ray dataset on detection of thoracic abnormalities[J/OL].[2020-10-15].http://arxiv.org/abs/2006.10550.