Computer Science ›› 2020, Vol. 47 ›› Issue (9): 129-134.doi: 10.11896/jsjkx.190700203

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability

ZHANG Jia-jia, ZHANG Xiao-hong   

  1. School of Big Data & Software Engineering,Chongqing University,Chongqing 400000,China
  • Received:2019-07-29 Published:2020-09-10
  • About author:ZHANG Jia-jia,born in 1994,postgra-duate.Her main research interests include medical image analysis,deep learning and so on.
    ZHANG Xiao-hong,born in 1973,professor.His main research interests include data mining of software enginee-ring,topic modeling,image semantic analysis and video analysis.
  • Supported by:
    National Natural Science Foundation of China (61772093) and Chongqing Major Theme Projects (cstc2018jszx-cyztzxX0017).

Abstract: The characteristics of lung nodules are complex and diverse,which make it difficult to classify lung nodules.Although more and more deep learning models are applied to the lung nodule classification task of computer-aided lung cancer diagnosis systems,the “black box” characteristics of these models cannot explain what knowledge the model has learned from the data and how the knowledge influences the decision,leading to a lack of reliability in the diagnosis results.To this end,an interpretable multi-branch convolutional neural network model is proposed to identify the benign and malignant lung nodules.The model uses the semantic features of the pulmonary nodules which radiologists use in diagnosis to assist identifying the benign and malignant lung nodules.These characteristics are combined with the branch of malignancy classification into a multi-branch network.Then beyond the malignancy classification,the model can predict nodule attributes,which could potentially explain the diagnosis result.Experimental results on the LIDC-IDRI dataset show that,compared with the existing methods,the proposed model can not only obtain interpretable diagnostic results,but also achieve better classification of lung nodules with an accuracy rate of 97.8%.

Key words: Computer-aided diagnosis, Convolutional neural network, Multi-branch, Interpretable, Classification of malignant degree of pulmonary nodules

CLC Number: 

  • TP183
[1] SIEGEL R L,MILLER K D,JEMAL A.Cancer statistics,2016[J].CA:A Cancer Journal for Clinicians,2016,66(1):7-30.
[2] SHEN S,HAN S X,PETOUSIS P,et al.A Bayesian model for estimating multi-state disease progression[J].Computers in Biology and Medicine,2017,81:111-120.
[3] National Lung Screening Trial Research Team.Reduced lung-cancer mortality with low-dose computed tomographic screening[J].New England Journal of Medicine,2011,365(5):395-409.
[4] ARMATO III S G,ALTMAN M B,WILKIE J,et al.Automated lung nodule classification following automated nodule detection on CT:A serial approach[J].Medical Physics,2003,30(6):1188-1197.
[5] SHEN S,BUI A A T,CONG J,et al.An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy[J].Computers in Biology and Medicine,2015,57:139-149.
[6] DUGGAN N,BAE E,SHEN S,et al.A technique for lung nodule candidate detection in CT using global minimization methods[C]//International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition.Cham:Springer, 2015:478-491.
[7] FIRMINO M,ANGELO G,MORAIS H,et al.Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy[J].Biomedical Engineering Online,2016,15(1):2.
[8] AMIR G J,LEHMANN H P.After detection:The improvedaccuracy of lung cancer assessment using radiologic computer-aided diagnosis[J].Academic Radiology,2016,23(2):186-191.
[9] HUANG P,PARK S,YAN R,et al.Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules:a matched case-control study[J].Radiology,2017,286(1):286-295.
[10] SHEN W,ZHOU M,YANG F,et al.Multi-scale Convolutional Neural Networks for Lung Nodule Classification[J].Inf Process Med Imaging,2015,24:588-599.
[11] SHEN W,ZHOU M,YANG F,et al.Multi-crop Convolutional Neural Networks for Lung Nodule Malignancy Suspiciousness Classification[J].Pattern Recognition,2016,61(61):663-673.
[12] PIEDRA E A R,TAIRA R K,EL-SADEN S,et al.Assessing variability in brain tumor segmentation to improve volumetric accuracy and characterization of change[C]// IEEE-EMBS International Conference on Biomedical & Health Informatics.IEEE,2016.
[13] DOU Q,CHEN H,YU L,et al.Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection[J].IEEE Transactions on Biomedical Engineering,2017,64(7):1558-1567.
[14] LIANG C H,YUE J Y,HAN D M,et al.Diagnostic value of lobulation sign in solitary pulmonary nodule by CT to determine.
benignity or malignancy[J].Medical Information(Section of Operative Surgery),2007(10):94-95.
[15] HUSSEIN S,CAO K,SONG Q,et al.Risk stratification of lung nodules using 3d cnn-based multi-task learning[C]//International Conference on Information Processing in Medical Imaging.Cham:Springer, 2017:249-260.
[16] KIM H,PARK C M,GOO J M,et al.Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer[J].Investigative Radiology,2015,50(9).
[17] ARMATO S,MCLENNAN G,M MCNITT-GRAY,et al.WE-B-201B-02:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI):A Completed Public Database of CT Scans for Lung Nodule Analysis[J].Medical Physics,2010,37(6Part6):3416-3417.
[18] MOLCHANOV P,TYREE S,KARRAS T,et al.Pruning Conv-olutional Neural Networks for Resource Efficient Inference[J].International Conference on Learning Representations,2017,2:324-332.
[19] CAUSEY J,ZHANG J Y,MA S Q,et al.Highly accurate model for prediction of lung nodule malignancy with CT scans[J].Scientific Reports,2018,8(1):9286.
[20] XIE Y,ZHANG J,XIA Y,et al.Fusing texture,shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT[J].Information Fusion,2018,42:102-110.
[1] SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing. L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing [J]. Computer Science, 2021, 48(1): 89-95.
[2] HE Yan-hui, WU Gui-xing, WU Zhi-qiang. Domain Alignment Based Object Detection of X-ray Images [J]. Computer Science, 2021, 48(1): 175-181.
[3] LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min. Survey on Target Site Prediction of Human miRNA Based on Deep Learning [J]. Computer Science, 2021, 48(1): 209-216.
[4] ZHU Ling-ying, SANG Qing-bing, GU Ting-ting. No-reference Stereo Image Quality Assessment Based on Disparity Information [J]. Computer Science, 2020, 47(9): 150-156.
[5] CUI Tong-tong, WANG Gui-ling, GAO Jing. Ship Trajectory Classification Method Based on 1DCNN-LSTM [J]. Computer Science, 2020, 47(9): 175-184.
[6] LIU Hai-chao, WANG Li. Graph Classification Model Based on Capsule Deep Graph Convolutional Neural Network [J]. Computer Science, 2020, 47(9): 219-225.
[7] LIANG Zheng-you, HE Jing-lin, SUN Yu. Three-dimensional Convolutional Neural Network Evolution Method for Facial Micro-expression Auto-recognition [J]. Computer Science, 2020, 47(8): 227-232.
[8] SUN Yan-li, YE Jiong-yao. Convolutional Neural Networks Compression Based on Pruning and Quantization [J]. Computer Science, 2020, 47(8): 261-266.
[9] LIU Xiao, YUAN Guan, ZHANG Yan-mei, YAN Qiu-yan, WANG Zhi-xiao. Hand Gesture Recognition Based on Self-adaptive Multi-classifiers Fusion [J]. Computer Science, 2020, 47(7): 103-110.
[10] CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks [J]. Computer Science, 2020, 47(6A): 70-74.
[11] HE Lei, SHAO Zhan-peng, ZHANG Jian-hua and ZHOU Xiao-long. Review of Deep Learning-based Action Recognition Algorithms [J]. Computer Science, 2020, 47(6A): 139-147.
[12] SUN Zheng and WANG Xin-yu. Application of Deep Learning in Photoacoustic Imaging [J]. Computer Science, 2020, 47(6A): 148-152.
[13] MA Hai-Jiang. Recommendation Algorithm Based on Convolutional Neural Network and Constrained Probability Matrix Factorization [J]. Computer Science, 2020, 47(6A): 540-545.
[14] WANG Yan, WANG Li. Local Gabor Convolutional Neural Network for Hyperspectral Image Classification [J]. Computer Science, 2020, 47(6): 151-156.
[15] WANG Hang, CHEN Xiao, TIAN Sheng-zhao, CHEN Duan-bing. SAR Image Recognition Based on Few-shot Learning [J]. Computer Science, 2020, 47(5): 124-128.
Full text



[1] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[2] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[3] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .
[4] YANG Yu-qi, ZHANG Guo-an and JIN Xi-long. Dual-cluster-head Routing Protocol Based on Vehicle Density in VANETs[J]. Computer Science, 2018, 45(4): 126 -130 .
[5] SHI Chao, XIE Zai-peng, LIU Han and LV Xin. Optimization of Container Deployment Strategy Based on Stable Matching[J]. Computer Science, 2018, 45(4): 131 -136 .
[6] HAN Kui-kui, XIE Zai-peng and LV Xin. Fog Computing Task Scheduling Strategy Based on Improved Genetic Algorithm[J]. Computer Science, 2018, 45(4): 137 -142 .
[7] ZHENG Xiu-lin, SONG Hai-yan and FU Yi-peng. Distinguishing Attack of MORUS-1280-128[J]. Computer Science, 2018, 45(4): 152 -156 .
[8] WU Shu, ZHOU An-min and ZUO Zheng. PDiOS:Private API Call Detection in iOS Applications[J]. Computer Science, 2018, 45(4): 163 -168 .
[9] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[10] LUO Xiao-yang, HUO Hong-tao, WANG Meng-si and CHEN Ya-fei. Passive Image-splicing Detection Based on Multi-residual Markov Model[J]. Computer Science, 2018, 45(4): 173 -177 .