Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 54-59.doi: 10.11896/jsjkx.210400211

• Smart Healthcare • Previous Articles     Next Articles

Automatic Detection of Pulmonary Nodules in Low-dose CT Images Based on Improved CNN

YUE Qing1, YIN Jian-yu2, WANG Sheng-sheng2   

  1. 1 School of Computer Science,Jilin Normal University,Siping,Jilin136000,China
    2 College of Computer Science and Technology,Jilin University,Changchun 130000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YUE Qing,born in 1994,master.Her main research interests include medical image analysis and machine learning.
    WANG Sheng-sheng,born in 1974,Ph.D,professor.His main research interests include the areas of computer vision,deep learning and data mining.

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Abstract: With air pollution getting worse and worse,lung cancer has become one of the malignant tumors with the fastest increasing morbidity and mortality rate,which seriously endangers people's life and health.The early stage of lung cancer is mainly in the form of pulmonary nodules.If the early stage of lung cancer can be detected and treated in time,the treatment effect of lung cancer will be improved.Low-dose spiral CT is widely used in the diagnosis of pulmonary nodules because of its characteristics of fast acquisition speed,low cost and low radiation.At present,CT image diagnosis mostly adopts the traditional manual diagnosis and CAD system diagnosis,but these two methods have the disadvantages of low accuracy and poor generalization.In view of the above problems,this paper takes the detection of pulmonary nodules in the field of medical assisted diagnosis as the research object,and proposes an improved low-dose CT image automatic detection algorithm for pulmonary nodules based on CNN.Firstly,the CT images are preprocessed to extract the lung parenchyma.Secondly,the cascade-rcnn candidate nodule screening network is improved to extract higher quality targets.Thirdly,an improved 3D CNN false positive reduction network is proposed to improve the accuracy of nodular classification.Finally,experiments are carried out on Luna16 dataset.Compared with existing algorithms,the detection accuracy of the proposed algorithm is improved.

Key words: 3D CNN, Cascade-RCNN, False positive reduction, LUNA16, Pulmonary nodule detection

CLC Number: 

  • TP391
[1] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2015[J].CA:A Cancer Journal for Clinicians,2015,65(1):5-29.
[2] ZHU W,VANG Y S,HUANG Y,et al.Deepem:Deep 3d convnets with em forweakly supervised pulmonary nodule detection[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2018:812-820.
[3] QIN Y,ZHENG H,ZHU Y M,et al.Simultaneous accurate detection of pulmonary nodules and false positive reduction using 3D CNNs[C]//2018 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2018:1005-1009.
[4] ZUO W,ZHOU F,LI Z,et al.Multi-resolution CNN and know-ledge transfer for candidate classification in lung nodule detection[J].IEEE Access,2019,7:32510-32521.
[5] DING J,LI A,HU Z,et al.Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2017:559-567.
[6] NASRULLAH N,SANG J,ALAM M S,et al.Automated lung nodule detection and classification using deep learning combined with multiple strategies[J].Sensors,2019,19(17):3722.
[7] JAME A.Reduced lung-cancer mortality with low-dose computed tomo graphic screening[J].The New England Journal of Medicine,2011,65(5):395-409.
[8] ZHANG Z,LI X,YOU Q,et al.Multicontext 3D residual CNN for false positive reduction of pulmonary nodule detection[J].International Journal of Imaging Systems and Technology,2019,29(1):42-49.
[9] JACOBS C,RIKXOORT E M,TWELLMANN T,et al.Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images[J].Medical Image Analysis,2014,18(2):374-384.
[10] ZUO Z,WANG G,SHUAI B,et al.Exemplar based deep discriminative and shareable feature learning for scene image classification[J].Pattern Recognition,2015,48(10):3004-3015.
[11] LONG F N,ZHU X S,GAN J Z.Ultrasound image segmentation of brachial plexus via convolutional neural networks[J].Journal of Hefei University of Technology(Natural Science),2018,41(9):1191-1195,1296.
[12] ZHU W,LIU C,FAN W,et al.Deeplung:Deep 3d dual pathnets for automated pulmonary nodule detection and classification[C]//2018 IEEE Winter Conference on Applications of Computer Vision(WACV).IEEE,2018:673-681.
[13] JIN H,LI Z,TONG R,et al.A deep 3D residual CNN for false positive reduction in pulmonary nodule detection[J].Medical Physics,2018,45(5):2097-2107.
[14] CAI Z,VASCONCELOS N.Cascade r-cnn:Delving into highquality object detection[C]//Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition.2018:6154-6162.
[15] JI S,XU W,YANG M,et al.3D convolutional neural networks for human action recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,35(1):221-231.
[16] SETIO A A,JACOBS C,GELDERBLOM J,et al.Automatic detection of large pulmonary solid nodules in thoracic CT images[J].Medical physics,2015,42(10):5642-5653.
[17] KRISHNAMURTHY S,NARASIMHAN G,RENGASAMYU.An automatic computerized model for cancerous lung nodule detection from computed tomography images with reduced false positives[C]//International Conference on Recent Trends in Image Processing and Pattern Recognition.Springer,Singapore,2016:343-355.
[18] GOLAN R,JACOB C,DENZINGER J.Lung nodule detection in CT images using deep convolutional neural networks[C]//2016 International Joint Conference on Neural Networks(IJCNN).IEEE.2016:243-250.
[19] SHEN W,ZHOU M,YANG F,et al.Multi-scale convolutional neural networks for lung nodule classification[C]//International Conference on Information Processing in Medical Imaging.Springer.2015:588-599.
[20] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[21] GAO H,ZHU X,LIN S,et al.Deformable kernels:Adapting effective receptive fields for object deformation[J].arXiv:1910.02940,2019.
[22] XU S,LU H,YE M,et al.Improved Cascade R-CNN for Medical Images of Pulmonary Nodules Detection Combining Dilated HRNet[C]//Proceedings of the 2020 12th International Confe-rence on Machine Learning and Computing.2020:283-288.
[23] ZHAO L,WANG J,LI X,et al.Deep convolutional neural networks with merge-and-run mappings[J].arXiv:1611.07718,2016.
[24] GAO S,CHENG M M,ZHAO K,et al.Res2net:A new multi-scale backbone architecture[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(2):652-662.
[25] GRUETZEMACHER R,GUPTA A.Using Deep Learning for Pulmonary Nodule Detection & Diagnosis[C]//Proceedings of the Twenty-Second Americas Conference on Information Systems,San Diego,CA,USA,2016.
[26] GU J,TIAN Z,QI Y.Pulmonary nodules detection based on deformable convolution[J].IEEE Access,2020,8:16302-16309.
[27] DOU Q,CHEN H,YU L,et al.Multi-level contextual 3D CNNs for false positive reduction in pulmonary nodule detection[J].IEEE Transactions on Biomedical Engineering,2017,64:1558-1567.
[28] PENG H,SUN H,GUO Y.3D multi-scale deep convolutionalneural networks for pulmonary nodule detection[J].Plos one,2021,16(1):e0244406.
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