{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T12:02:35Z","timestamp":1782561755708,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.<\/jats:p>","DOI":"10.3390\/s20102837","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T02:43:42Z","timestamp":1589769822000},"page":"2837","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Multi-Level Cross Residual Network for Lung Nodule Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1366-1807","authenticated-orcid":false,"given":"Juan","family":"Lyu","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"College of Information Engineering, Minzu University of China, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0849-5098","authenticated-orcid":false,"given":"Sai Ho","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"ref_1","unstructured":"Ferlay, J., Ervik, M., Lam, F., Colombet, M., Mery, L., Pi neros, M., Znaor, A., Soerjomataram, I., and Bray, F. (2018). Global Cancer Observatory: Cancer Today, International Agency for Research on Cancer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3322\/caac.21557","article-title":"Cancer screening in the United States, 2019: A review of current American Cancer Society guidelines and current issues in cancer screening","volume":"69","author":"Smith","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"69S","DOI":"10.1378\/chest.07-1349","article-title":"Screening for lung cancer: ACCP evidence-based clinical practice guidelines","volume":"132","author":"Bach","year":"2007","journal-title":"Chest"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"170025","DOI":"10.1183\/16000617.0025-2017","article-title":"Lung nodules: Size still matters","volume":"26","author":"Larici","year":"2017","journal-title":"Eur. Respir. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1148\/radiol.2462070712","article-title":"Fleischner Society: Glossary of terms for thoracic imaging","volume":"246","author":"Hansell","year":"2008","journal-title":"Radiology"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bakator, M., and Radosav, D. (2018). Deep learning and medical diagnosis: A review of literature. Multimodal Technol. Interact., 2.","DOI":"10.3390\/mti2030047"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1002\/mp.13264","article-title":"Deep learning in medical imaging and radiation therapy","volume":"46","author":"Sahiner","year":"2019","journal-title":"Med. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_11","unstructured":"Minaee, S., and Abdolrashidi, A. (2019). Deep-emotion: Facial expression recognition using attentional convolutional network. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10916-018-1072-9","article-title":"Generative adversarial network for medical images (MI-GAN)","volume":"42","author":"Iqbal","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Minaee, S., Wang, Y., Choromanska, A., Chung, S., Wang, X., Fieremans, E., Flanagan, S., Rath, J., and Lui, Y.W. (2018, January 18\u201321). A deep unsupervised learning approach toward MTBI identification using diffusion MRI. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512556"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Halder, A., Dey, D., and Sadhu, A.K. (2020). Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: A Comprehensive Review. J. Digit. Imaging, 1\u201323.","DOI":"10.1007\/s10278-020-00320-6"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TMI.2019.2905917","article-title":"MTBI identification from diffusion MR images using bag of adversarial visual features","volume":"38","author":"Minaee","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","unstructured":"Shen, W., Zhou, M., Yang, F., Yang, C., and Tian, J. (July, January 28). Multi-scale convolutional neural networks for lung nodule classification. Proceedings of the International Conference on Information Processing in Medical Imaging, Isle of Skye, UK."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","article-title":"Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification","volume":"61","author":"Shen","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"da N\u00f3brega, R.V.M., Peixoto, S.A., da Silva, S.P.P., and Rebou\u00e7as Filho, P.P. (2018, January 18\u201321). Lung nodule classification via deep transfer learning in CT lung images. Proceedings of the IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden.","DOI":"10.1109\/CBMS.2018.00050"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dey, R., Lu, Z., and Hong, Y. (2018, January 4\u20137). Diagnostic classification of lung nodules using 3D neural networks. Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s11548-019-01981-7","article-title":"Lung nodule classification using deep Local\u2014Global networks","volume":"14","author":"Lan","year":"2019","journal-title":"Int. J. Comp. Assist. Radiol. Surg."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"El-Regaily, S.A., Salem, M.A.M., Aziz, M.H.A., and Roushdy, M.I. (2019). Multi-view Convolutional Neural Network for lung nodule false positive reduction. Expert Syst. Appl., 113017.","DOI":"10.1016\/j.eswa.2019.113017"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/TMI.2019.2894349","article-title":"Lung and pancreatic tumor characterization in the deep learning era: Novel supervised and unsupervised learning approaches","volume":"38","author":"Hussein","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TMI.2019.2934577","article-title":"Multi-Task Deep Model with Margin Ranking Loss for Lung Nodule Analysis","volume":"39","author":"Liu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lyu, J., and Ling, S.H. (2018, January 18\u201321). Using multi-level convolutional neural network for classification of lung nodules on CT images. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512376"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans","volume":"38","author":"Armato","year":"2011","journal-title":"Med. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 8\u201316). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_31","unstructured":"Reeves, A.P., and Biancardi, A.M. (2020, May 15). The Lung Image Database Consortium (Lidc) Nodule Size Report. Available online: http:\/\/www.via.cornell.edu\/lidc\/."},{"key":"ref_32","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dong, H., Supratak, A., Mai, L., Liu, F., Oehmichen, A., Yu, S., and Guo, Y. (2017, January 23\u201327). Tensorlayer: A versatile library for efficient deep learning development. Proceedings of the 25th ACM international conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3129391"},{"key":"ref_34","unstructured":"Rubinstein, R., and Kroese, D. (2004). The Cross-Entropy method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine-Learning, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2837\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:29:29Z","timestamp":1760174969000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,16]]},"references-count":35,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102837"],"URL":"https:\/\/doi.org\/10.3390\/s20102837","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,16]]}}}