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On-line Access: 2022-11-15
Received: 2021-12-13
Revision Accepted: 2022-05-03
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https://orcid.org/0000-0001-7825-4455
A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis
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Dachuan GAO, Xiaodan YE, Xuewen HOU, Yang CHEN, Xue KONG, Yuanzhong XIE, Shengdong NIE. A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis[J]. Journal of Zhejiang University Science B, 2022, 23(11): 957-967.
@article{title="A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis",
author="Dachuan GAO, Xiaodan YE, Xuewen HOU, Yang CHEN, Xue KONG, Yuanzhong XIE, Shengdong NIE",
journal="Journal of Zhejiang University Science B",
volume="23",
number="11",
pages="957-967",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B2101009"
}
%0 Journal Article
%T A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis
%A Dachuan GAO
%A Xiaodan YE
%A Xuewen HOU
%A Yang CHEN
%A Xue KONG
%A Yuanzhong XIE
%A Shengdong NIE
%J Journal of Zhejiang University SCIENCE B
%V 23
%N 11
%P 957-967
%@ 1673-1581
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B2101009
TY - JOUR
T1 - A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis
A1 - Dachuan GAO
A1 - Xiaodan YE
A1 - Xuewen HOU
A1 - Yang CHEN
A1 - Xue KONG
A1 - Yuanzhong XIE
A1 - Shengdong NIE
J0 - Journal of Zhejiang University Science B
VL - 23
IS - 11
SP - 957
EP - 967
%@ 1673-1581
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B2101009
Abstract: In the USA, there were about 1 806 590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.
[1]AlbertRH, RussellJJ, 2009. Evaluation of the solitary pulmonary nodule. Am Fam Physician, 80(8):827-831.
[2]ArmatoSG III, McLennanG, BidautL, et al., 2011. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 38(2):915-931.
[3]BergstraJ, BengioY, 2012. Random search for hyper-parameter optimization. J Mach Learn Res, 13:281-305.
[4]CaoP, LiuXL, YangJZ, et al., 2017. A multi-kernel based framework for heterogeneous feature selection and over-sampling for computer-aided detection of pulmonary nodules. Pattern Recogn, 64:327-346.
[5]CauseyJL, ZhangJY, MaSQ, et al., 2018. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep, 8:9286.
[6]ChenYP, LiJN, XiaoHX, et al., 2017. Dual path networks. Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, p.4470-4478.
[7]DeyR, LuZJ, HongY, 2018. Diagnostic classification of lung nodules using 3D neural networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA. IEEE, p.774-778.
[8]DharaAK, MukhopadhyayS, DuttaA, et al., 2016. A combination of shape and texture features for classification of pulmonary nodules in lung CT images. J Digit Imaging, 29(4):466-475.
[9]Díaz-UriarteR, de AndrésSA, 2006. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7:3.
[10]GongJ, LiuJY, SunXW, et al., 2018. Computer-aided diagnosis of lung cancer: the effect of training data sets on classification accuracy of lung nodules. Phys Med Biol, 63(3):035036.
[11]GouldMK, DoningtonJ, LynchWR, et al., 2013. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. CHEST, 143(5 Suppl):e93S-e120S.
[12]HeKM, ZhangXY, RenSQ, et al., 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA. IEEE, p.770-778.
[13]HuangG, LiuZ, van der MaatenL, et al., 2017. Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. IEEE, p.2261-2269.
[14]KingmaDP, BaJ, 2015. Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, San Diego.
[15]KrizhevskyA, SutskeverI, HintonGE, 2017. ImageNet classification with deep convolutional neural networks. Commun ACM, 60(6):84-90.
[16]LuD, WengQ, 2007. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens, 28(5):823-870.
[17]LyuJ, BiXJ, LingSH, 2020. Multi-level cross residual network for lung nodule classification. Sensors, 20(10):2837.
[18]MastouriR, KhlifaN, NejiH, et al., 2021. A bilinear convolutional neural network for lung nodules classification on CT images. Int J Comput Assist Radiol Surg, 16(1):91-101.
[19]McHughML, 2012. Interrater reliability: the kappa statistic. Biochem Med, 22(3):276-282.
[20]ShenDG, WuGR, SukHI, 2017. Deep learning in medical image analysis. Annu Rev Biomed Eng, 19:221-248.
[21]ShenW, ZhouM, YangF, et al., 2017. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn, 61:663-673.
[22]SiegelRL, MillerKD, Goding SauerA, et al., 2020. Colorectal cancer statistics, 2020. CA Cancer J Clin, 70(3):145-164.
[23]SnoeckxA, ReyntiensP, DesbuquoitD, et al., 2018. Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights Imaging, 9(1):73-86.
[24]van ErkelAR, PattynamaPM, 1998. Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol, 27(2):88-94.
[25]WuWH, HuHH, GongJ, et al., 2019. Malignant-benign classification of pulmonary nodules based on random forest aided by clustering analysis. Phys Med Biol, 64(3):035017.
[26]ZhangF, SongY, CaiWD, et al., 2013. Context curves for classification of lung nodule images. 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, TAS, Australia. IEEE, p.1-7.
[27]ZhuWT, LiuCC, FanW, et al., 2018. DeepLung: deep 3D dual path nets for automated pulmonary nodule detection and classification. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA. IEEE, p.673-681.
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