{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:46:55Z","timestamp":1779360415657,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T00:00:00Z","timestamp":1580688000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multidim Syst Sign Process"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s11045-020-00703-6","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T19:05:19Z","timestamp":1580756719000},"page":"1163-1183","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography"],"prefix":"10.1007","volume":"31","author":[{"given":"Yi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kum Ju","family":"Chae","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Younhee","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gong Yong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9287-317X","authenticated-orcid":false,"given":"Seok-Bum","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,2,3]]},"reference":[{"key":"703_CR1","unstructured":"American Cancer Society. 2017. Retrieved January, 2019 from Cancer facts & figures 2017. https:\/\/www.cancer.org\/research\/cancer-facts-statistics\/all-cancer-facts-figures\/cancer-facts-figures-2017.html"},{"issue":"5","key":"703_CR2","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","volume":"35","author":"M Anthimopoulos","year":"2016","unstructured":"Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016). Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Transactions on Medical Imaging, 35(5), 1207\u20131216. https:\/\/doi.org\/10.1109\/TMI.2016.2535865.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"703_CR3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1117\/1.JMI.3.4.044506","volume":"3","author":"SG Armato","year":"2016","unstructured":"Armato, S. G., Drukker, K., Li, F., Hadjiiski, L., Tourassi, G. D., Kirby, J. S., et al. (2016). LUNGx challenge for computerized lung nodule classification. Journal of Medical Imaging, 3, 3\u20139. https:\/\/doi.org\/10.1117\/1.JMI.3.4.044506.","journal-title":"Journal of Medical Imaging"},{"issue":"5","key":"703_CR4","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1148\/radiographics.19.5.g99se181303","volume":"19","author":"SG Armato","year":"1999","unstructured":"Armato, S. G., Giger, M. L., Moran, C. J., Blackburn, J. T., Doi, K., & MacMahon, H. (1999). Computerized detection of pulmonary nodules on CT scans. RadioGraphics, 19(5), 1303\u20131311.","journal-title":"RadioGraphics"},{"key":"703_CR5","volume-title":"Pattern recognition and machine learning (information science and statistics)","author":"CM Bishop","year":"2006","unstructured":"Bishop, C. M. (2006). Pattern recognition and machine learning (information science and statistics). Secaucus, NJ: Springer."},{"key":"703_CR6","doi-asserted-by":"crossref","unstructured":"Chen, J., & Shen, Y. (2017). The effect of kernel size of CNNs for lung nodule classification. In 2017 9th international conference on advanced infocomm technology (ICAIT) (pp. 340\u2013344). https:\/\/doi.org\/10.1109\/ICAIT.2017.8388942.","DOI":"10.1109\/ICAIT.2017.8388942"},{"issue":"4","key":"703_CR7","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1109\/TMI.2014.2371821","volume":"34","author":"F Ciompi","year":"2015","unstructured":"Ciompi, F., Jacobs, C., Scholten, E. T., Wille, M. M. W., de Jong, P. A., Prokop, M., et al. (2015). Bag-of-frequencies: A descriptor of pulmonary nodules in computed tomography images. IEEE Transactions on Medical Imaging, 34(4), 962\u2013973. https:\/\/doi.org\/10.1109\/TMI.2014.2371821.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"703_CR8","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition (pp. 248\u2013255)."},{"key":"703_CR9","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12, 2121\u20132159.","journal-title":"Journal of Machine Learning Research"},{"key":"703_CR10","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-4541-9","volume-title":"An introduction to the bootstrap. Monographs on statistics and applied probability (Series)","author":"B Efron","year":"1993","unstructured":"Efron, B. (1993). An introduction to the bootstrap. Monographs on statistics and applied probability (Series) (Vol. 57). New York: Chapman & Hall."},{"issue":"4","key":"703_CR11","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1097\/00004424-199404000-00013","volume":"29","author":"ML Giger","year":"1994","unstructured":"Giger, M. L., Bae, K. T., & Macmahon, H. (1994). Computerized detection of pulmonary nodules in computed tomography images. Investigative Radiology, 29(4), 459\u2013465.","journal-title":"Investigative Radiology"},{"key":"703_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015a). Deep residual learning for image recognition. CoRR arXiv:1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"703_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015b). Deep residual learning for image recognition. CoRR arXiv:1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"703_CR14","doi-asserted-by":"publisher","first-page":"29","DOI":"10.4103\/2153-3539.186902","volume":"7","author":"A Janowczyk","year":"2016","unstructured":"Janowczyk, A., & Madabhushi, A. (2016). Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics, 7, 29. https:\/\/doi.org\/10.4103\/2153-3539.186902.","journal-title":"Journal of Pathology Informatics"},{"key":"703_CR15","unstructured":"Jarrett, K., Kavukcuoglu, K., Ranzato, M., & LeCun, Y. (2009). What is the best multi-stage architecture for object recognition? In 2009 IEEE 12th international conference on computer vision (pp. 2146\u20132153)."},{"key":"703_CR16","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093.","DOI":"10.1145\/2647868.2654889"},{"issue":"1","key":"703_CR17","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s11604-013-0264-y","volume":"32","author":"A Kamiya","year":"2014","unstructured":"Kamiya, A., Murayama, S., Kamiya, H., Yamashiro, T., Oshiro, Y., & Tanaka, N. (2014). Kurtosis and skewness assessments of solid lung nodule density histograms: Differentiating malignant from benign nodules on CT. Japanese Journal of Radiology, 32(1), 14\u201321.","journal-title":"Japanese Journal of Radiology"},{"key":"703_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0188290","volume":"12","author":"G Kang","year":"2017","unstructured":"Kang, G., Liu, K., Hou, B., & Zhang, N. (2017). 3D multi-view convolutional neural networks for lung nodule classification. PLOS ONE, 12, 1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0188290.","journal-title":"PLOS ONE"},{"key":"703_CR19","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th international conference on neural information processing systems\u2014volume 1, Curran Associates Inc., USA, NIPS\u201912 (pp. 1097\u20131105)."},{"key":"703_CR20","doi-asserted-by":"crossref","unstructured":"Levi, G., & Hassncer, T. (2015). Age and gender classification using convolutional neural networks. In 2015 IEEE conference on computer vision and pattern recognition workshops (CVPRW) (pp. 34\u201342). https:\/\/doi.org\/10.1109\/CVPRW.2015.7301352.","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"703_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., Diao, Y., Ma, H., & Li, Y. (2008). A statistical PCA method for face recognition. In 2008 Second international symposium on intelligent information technology application (vol. 3, pp. 376\u2013380). https:\/\/doi.org\/10.1109\/IITA.2008.71.","DOI":"10.1109\/IITA.2008.71"},{"key":"703_CR22","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., & Chen, M. (2014). Medical image classification with convolutional neural network. In 2014 13th international conference on control automation robotics vision (ICARCV) (pp. 844\u2013848).","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"703_CR23","doi-asserted-by":"publisher","first-page":"6215085","DOI":"10.1155\/2016\/6215085","volume":"2016","author":"W Li","year":"2016","unstructured":"Li, W., Cao, P., Zhao, D., & Wang, J. (2016). Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Computational and Mathematical Methods in Medicine, 2016, 6215085. https:\/\/doi.org\/10.1155\/2016\/6215085.","journal-title":"Computational and Mathematical Methods in Medicine"},{"issue":"2","key":"703_CR24","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","volume":"21","author":"MA Mazurowski","year":"2008","unstructured":"Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., & Tourassi, G. D. (2008). Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks, 21(2), 427\u2013436. (Advances in Neural Networks Research: IJCNN \u201907) .","journal-title":"Neural Networks"},{"issue":"1","key":"703_CR25","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12938-018-0529-x","volume":"17","author":"P Monkam","year":"2018","unstructured":"Monkam, P., Qi, S., Xu, M., Han, F., Zhao, X., & Qian, W. (2018). CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images. BioMedical Engineering OnLine, 17(1), 96. https:\/\/doi.org\/10.1186\/s12938-018-0529-x.","journal-title":"BioMedical Engineering OnLine"},{"issue":"3","key":"703_CR26","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.acra.2016.11.007","volume":"24","author":"M Nishio","year":"2017","unstructured":"Nishio, M., & Nagashima, C. (2017). Computer-aided diagnosis for lung cancer: Usefulness of nodule heterogeneity. Academic Radiology, 24(3), 328\u2013336.","journal-title":"Academic Radiology"},{"key":"703_CR27","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","volume":"61","author":"W Shen","year":"2017","unstructured":"Shen, W., Zhou, M., Yang, F., Yu, D., Dong, D., Yang, C., et al. (2017). Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition, 61, 663\u2013673. https:\/\/doi.org\/10.1016\/j.patcog.2016.05.029.","journal-title":"Pattern Recognition"},{"issue":"5","key":"703_CR28","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H Shin","year":"2016","unstructured":"Shin, H., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., et al. (2016). Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging, 35(5), 1285\u20131298. https:\/\/doi.org\/10.1109\/TMI.2016.2528162.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"703_CR29","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484\u2013489.","journal-title":"Nature"},{"key":"703_CR30","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556."},{"key":"703_CR31","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1155\/2017\/8314740","volume":"2017","author":"Q Song","year":"2017","unstructured":"Song, Q., Zhao, L., Luo, X., & Dou, X. (2017). Using deep learning for classification of lung nodules on computed tomography images. Journal of Healthcare Engineering, 2017, 7. https:\/\/doi.org\/10.1155\/2017\/8314740.","journal-title":"Journal of Healthcare Engineering"},{"issue":"1","key":"703_CR32","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929\u20131958.","journal-title":"The Journal of Machine Learning Research"},{"key":"703_CR33","unstructured":"Szegedy, C., Ioffe, S., & Vanhoucke, V. (2016). Inception-v4, inception-resnet and the impact of residual connections on learning. CoRR. arXiv:1602.07261."},{"key":"703_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015a). Going deeper with convolutions. In Computer vision and pattern recognition (CVPR).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"703_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015b). Rethinking the inception architecture for computer vision. CoRR arXiv:1512.00567.","DOI":"10.1109\/CVPR.2016.308"},{"issue":"5","key":"703_CR36","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., et al. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging, 35(5), 1299\u20131312.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"5","key":"703_CR37","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","volume":"365","author":"The National Lung Screening Trial Research Team","year":"2011","unstructured":"The National Lung Screening Trial Research Team. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5), 395\u2013409.","journal-title":"New England Journal of Medicine"},{"issue":"8","key":"703_CR38","doi-asserted-by":"publisher","first-page":"189","DOI":"10.4329\/wjr.v7.i8.189","volume":"7","author":"EJ van Beek","year":"2015","unstructured":"van Beek, E. J., Mirsadraee, S., & Murchison, J. T. (2015). Lung cancer screening: Computed tomography or chest radiographs? World Journal of Radiology, 7(8), 189\u2013193. https:\/\/doi.org\/10.4329\/wjr.v7.i8.189.","journal-title":"World Journal of Radiology"},{"key":"703_CR39","unstructured":"van Ginneken, B., Setio, A.\u00a0A.\u00a0A., Jacobs, C., & Ciompi, F. (2015). Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI) (pp. 286\u2013289)."},{"issue":"4","key":"703_CR40","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/TBME.2013.2295593","volume":"61","author":"F Zhang","year":"2014","unstructured":"Zhang, F., Song, Y., Cai, W., Lee, M. Z., Zhou, Y., Huang, H., et al. (2014). Lung nodule classification with multilevel patch-based context analysis. IEEE Transactions on Biomedical Engineering, 61(4), 1155\u20131166.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"4","key":"703_CR41","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11548-017-1696-0","volume":"13","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., & Qian, W. (2018). Agile convolutional neural network for pulmonary nodule classification using CT images. International Journal of Computer Assisted Radiology and Surgery, 13(4), 585\u2013595. https:\/\/doi.org\/10.1007\/s11548-017-1696-0.","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"key":"703_CR42","first-page":"8","volume":"9413","author":"H Zhu","year":"2015","unstructured":"Zhu, H., Cheng, H., & Fan, Y. (2015). Random local binary pattern based label learning for multi-atlas segmentation. ProcSPIE, 9413, 8.","journal-title":"ProcSPIE"}],"container-title":["Multidimensional Systems and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-020-00703-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11045-020-00703-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11045-020-00703-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T05:29:16Z","timestamp":1695706156000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11045-020-00703-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,3]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["703"],"URL":"https:\/\/doi.org\/10.1007\/s11045-020-00703-6","relation":{},"ISSN":["0923-6082","1573-0824"],"issn-type":[{"value":"0923-6082","type":"print"},{"value":"1573-0824","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,3]]},"assertion":[{"value":"1 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}