{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:00:11Z","timestamp":1778122811603,"version":"3.51.4"},"reference-count":141,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,7]],"date-time":"2019-01-07T00:00:00Z","timestamp":1546819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Liaoning Doctoral Research Foundation of China","award":["20170520238"],"award-info":[{"award-number":["20170520238"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.<\/jats:p>","DOI":"10.3390\/s19010194","type":"journal-article","created":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T03:06:06Z","timestamp":1547003166000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images"],"prefix":"10.3390","volume":"19","author":[{"given":"Xinqi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1243-0123","authenticated-orcid":false,"given":"Keming","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Norman Bethune Health Science Center of Jilin University, No. 2699 Qianjin Street, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Duo","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21387","article-title":"Cancer statistics","volume":"67","author":"Siegel","year":"2017","journal-title":"CA A Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3322\/caac.21338","article-title":"Cancer statistics in China","volume":"66","author":"Chen","year":"2015","journal-title":"CA A Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.bspc.2017.11.017","article-title":"An appraisal of nodules detection techniques for lung cancer in CT images","volume":"41","author":"Rehman","year":"2018","journal-title":"Biomed. Signal Proc. Control"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.2214\/ajr.126.6.1233","article-title":"Lesion conspicuity, tructured noise, and film reader error","volume":"126","author":"Kundel","year":"1976","journal-title":"Am. J. Roentgenol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1097\/00004424-199002000-00006","article-title":"Satisfaction of search in diagnostic radiology","volume":"25","author":"Berbaum","year":"1990","journal-title":"Investig. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1148\/radiology.183.1.1549661","article-title":"Classification and lessons in 182 cases presented at a problem case conference","volume":"183","author":"Renfrew","year":"1992","journal-title":"Radiology"},{"key":"ref_7","first-page":"1","article-title":"Computer-aided detection system for lung cancer in computed tomography scans: A review","volume":"13","author":"Firmino","year":"2017","journal-title":"Curr. Med. Imaging Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cmpb.2015.10.006","article-title":"Automatic 3D pulmonary nodule detection in CT images","volume":"124","author":"Valente","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_10","unstructured":"(2018, January 11). Global Computer-Aided Detection (CAD) Market US$ 2.2 Billion by 2023. Available online: https:\/\/www.ihealthcareanalyst.com\/pre-screening-diagnostic-technology-adoption-computer-aided-detection-market\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TITB.2007.899504","article-title":"3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images","volume":"12","author":"Diciotti","year":"2008","journal-title":"Inf. Technol. Biomed."},{"key":"ref_12","unstructured":"(2018, May 18). ELCAP Public Lung Image Database. Available online: http:\/\/www.via.cornell.edu\/databases\/lungdb.html."},{"key":"ref_13","unstructured":"Armato, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., and Clarke, L.P. (2018, May 20). Data From LIDC-IDRI. The Cancer Imaging Archive. Available online: http:\/\/doi.org\/10.7937\/K9\/TCIA.2015.LO9QL9SX."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11547-008-0282-5","article-title":"MAGIC-5: An Italian mammographic database of digitised images for research","volume":"113","author":"Tangaro","year":"2008","journal-title":"La Radiol. Med."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0140-6736(99)06093-6","article-title":"Early Lung Cancer Action Project: Overall design and findings from baseline screening","volume":"354","author":"Henschke","year":"1999","journal-title":"Lancet"},{"key":"ref_17","unstructured":"Zhang, F., Cai, W.D., Song, Y., Lee, M.Z., Shan, S., and Feng, D.D. (2013, January 3\u20137). Overlapping node discovery for improving classification of lung nodules. Proceedings of the EMBC, Osaka, Japan."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.patcog.2017.12.022","article-title":"Multi-view multi-scale CNNs for lung nodule type classification from CT images","volume":"77","author":"Liu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","article-title":"Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules","volume":"174","author":"Shiraishi","year":"2000","journal-title":"Am. J. Roentgenol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1102\/1470-7330.2011.9020","article-title":"NELSON lung cancer screening study","volume":"11","author":"Zhao","year":"2011","journal-title":"Cancer Imaging."},{"key":"ref_21","unstructured":"(2015, May 27). Consortium for Open Medical Image Computing, Automatic Nodule Detection. Available online: http:\/\/anode09.grand-challenge.org\/."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.media.2010.05.005","article-title":"Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study","volume":"14","author":"Ginneken","year":"2010","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"044506","DOI":"10.1117\/1.JMI.3.4.044506","article-title":"LUNGx Challenge for computerized lung nodule classification","volume":"3","author":"Armato","year":"2016","journal-title":"J. Med. Imaging"},{"key":"ref_24","unstructured":"(2018, May 12). SPIE-AAPM Lung CT Challenge. Available online: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/SPIE-AAPM+Lung+CT+Challenge."},{"key":"ref_25","unstructured":"(2018, May 12). NSCLC-Radiomics-Genomics. Available online: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/NSCLC-Radiomics-Genomics."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1038\/ncomms5006","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"ref_27","unstructured":"(2018, May 15). LUNA16\u2014Data. Available online: https:\/\/luna16.grand-challenge.org\/data\/."},{"key":"ref_28","unstructured":"(2012, May 18). Danish Lung Nodule Screening Trial (DLCST), Available online: https:\/\/clinicaltrials.gov\/ct2\/show\/study\/NCT00496977."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jiang, H.Y., Ma, H., Qian, W., Wei, G.H., Zhao, X.Z., and Gao, M. (2017, January 11\u201315). A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea.","DOI":"10.1109\/EMBC.2017.8036885"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ma, J.C., Wang, Q., Ren, Y., Hu, H., and Zhao, J. (2016, January 5). Automatic Lung Nodule Classification with Radiomics Approach. Proceedings of the Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, San Diego, CA, USA.","DOI":"10.1117\/12.2220768"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Song, J.Q., Liu, H., Geng, F.H., and Zhang, C.M. (2016, January 8\u201312). Weakly-Supervised Classification of Pulmonary Nodules Based on Shape Characters. Proceedings of the 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), Auckland, New Zealand.","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.58"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.compmedimag.2005.04.009","article-title":"Computer-aided diagnosis: A shape classification of pulmonary nodules imaged by high-resolution CT","volume":"29","author":"Iwano","year":"2005","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1118\/1.598603","article-title":"A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: Preliminary results","volume":"26","author":"Har","year":"1999","journal-title":"Med. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dhara, A.K., Mukhopadhyay, S., Dutta, A., Garg, M., Khandelwal, N., and Kumar, P. (2016, January 24). Classification of pulmonary nodules in lung CT images using shape and texture features. Medical Imaging. Proceedings of the Computer-Aided Diagnosis, San Diego, CA, USA.","DOI":"10.1117\/12.2214466"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rendon-Gonzalez, E., and Ponomaryov, V. (2016, January 20\u201324). Automatic Lung nodule segmentation and classification in CT images based on SVM. Proceedings of the 2016 9th International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW), Kharkiv, Ukraine.","DOI":"10.1109\/MSMW.2016.7537995"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, X., Dong, D., Song, J.D., Xu, M., Zang, Y.L., and Tian, J. (2016, January 16\u201320). Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590938"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Chang, C.K., Tu, C.Y., Liao, W.C., Wu, B.R., Chou, K.T., Chiou, Y.R., Yang, S.N., Zhang, G., and Huang, T.C. (2018). Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192002"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, H.H., and Ni, S.D. (2017, January 29\u201331). Classification of malignant-benign pulmonary nodules in lung CT images using an improved random forest. Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Guilin, China.","DOI":"10.1109\/FSKD.2017.8393127"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.4304\/jcp.8.9.2245-2255","article-title":"Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases","volume":"8","author":"Aggarwal","year":"2013","journal-title":"J. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mukherjee, J., Chakrabarti, A., Skaikh, S.H., and Kar, M. (2014, January 19\u201321). Automatic Detection and Classification of Solitary Pulmonary Nodules from Lung CT Images. Proceedings of the 2014 Fourth International Conference of Emerging Applications of Information Technology, Kolkata, India.","DOI":"10.1109\/EAIT.2014.64"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1007\/s11548-017-1626-1","article-title":"Feature fusion for lung nodule classification","volume":"12","author":"Amal","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, Y.T., Xia, Y., Zhang, J.P., Feng, D.D., Fulham, M.J., and Cai, W.D. (2017, January 11\u201313). Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_75"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TMI.2016.2629462","article-title":"Automatic Scoring of Multiple Semantic Attributes with Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images","volume":"36","author":"Chen","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, Y.T., Zhang, J.P., Liu, S.D., Cai, W.D., and Xia, Y. (2016, January 21). Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features. Proceedings of the Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging, Athens, Greece.","DOI":"10.1007\/978-3-319-61188-4_11"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.inffus.2017.10.005","article-title":"Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT","volume":"42","author":"Xie","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"035036","DOI":"10.1088\/1361-6560\/aaa610","article-title":"Computer-aided diagnosis of lung cancer: The effect of training data sets on classification accuracy of lung nodules","volume":"63","author":"Gong","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1007\/s10278-015-9857-6","article-title":"A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images","volume":"29","author":"Dhara","year":"2016","journal-title":"J. Digit. Imaging"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1097\/RCT.0000000000000394","article-title":"Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2-and 3-Dimensional Analysis","volume":"40","author":"Gierada","year":"2016","journal-title":"Comput. Assist. Tomogr."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fernandes, V.P.M., Kanehisa, R.F.A., Junior, G.B., Silva, A.C., and de Paiva, D.C. (2016, January 4\u20138). Lung nodule classification based on shape distributions. Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy.","DOI":"10.1145\/2851613.2851877"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dilger, S.K., Judisch, A., Uthoff, J., Hammond, E., Newell, J.D., and Sieren, J.C. (2015, January 21\u201326). Improved pulmonary nodule classification utilizing lung parenchyma texture features. Proceedings of the Medical Imaging: Computer-Aided Diagnosis, Orlando, FL, USA.","DOI":"10.1117\/12.2081397"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"El-Baz, A., Nitzken, M., Vanbogaert, E., Gimel\u2019farb, G.L., Falk, R., and El-Ghar, M.A. (April, January 30). A novel shape-based diagnostic approach for early diagnosis of lung nodules. Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA.","DOI":"10.1109\/ISBI.2011.5872373"},{"key":"ref_52","first-page":"772","article-title":"3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules","volume":"22","author":"Nitzken","year":"2011","journal-title":"Inf. Process. Med. Imaging"},{"key":"ref_53","unstructured":"Namin, S.T., Moghaddam, H.A., Jafari, R., Esmaeil-Zadeh, M., and Gity, M. (2010, January 10\u201313). Automated detection and classification of pulmonary nodules in 3D thoracic CT images. Proceedings of the 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC), Istanbul, Turkey."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3086","DOI":"10.1118\/1.3140589","article-title":"Computer-aided diagnosis of pulmonary nodules on CT scans: Improvement of classification performance with nodule surface features","volume":"36","author":"Way","year":"2009","journal-title":"Med. Phys."},{"key":"ref_55","first-page":"183","article-title":"Classification of lung nodules in diagnostic CT: An approach based on 3-D vascular features, nodule density distributions, and shape features","volume":"5032","author":"Lo","year":"2003","journal-title":"SPIE"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/S1076-6332(03)00507-5","article-title":"Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images","volume":"10","author":"Kawata","year":"2003","journal-title":"Acad. Radiol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kawata, Y., Niki, N., Ohmatsu, H.Y., Kusumoto, M., Kakinuma, R., Mori, L., Nishiyama, H., Eguchi, K., Kaneko, M., and Moriyama, N. (2000, January 11\u201314). Hybrid Classification Approach of Malignant and Benign Pulmonary Nodules Based on Topological and Histogram Features. Proceedings of the MICCAI 2000: Medical Image Computing and Computer-Assisted Intervention, Pittsburgh, PA, USA.","DOI":"10.1007\/978-3-540-40899-4_30"},{"key":"ref_58","first-page":"1796","article-title":"Computer-aided differential diagnosis of pulmonary nodules based on a hybrid classification approach","volume":"4322","author":"Kawata","year":"2001","journal-title":"Med. Imaging: Image Process."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wyckoff, N., McNitt-Gray, M.F., Goldin, J.G., Suh, R.D., Sayre, J.W., and Aberle, D.R. (2000). Classification of solitary pulmonary nodules (SPNs) imaged on high-resolution CT using contrast enhancement and three-dimensional quantitative image features. Med. Imaging Image Process., 1107\u20131115.","DOI":"10.1117\/12.387615"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Xie, Y.T., Xia, Y., Zhang, J.P., Song, Y., Feng, D.G., Fulham, M., and Cai, W.D. (2018). Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT. IEEE Trans. Med. Imaging.","DOI":"10.1109\/TMI.2018.2876510"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2017.08.026","article-title":"Lung nodule classification using local kernel regression models with out-of-sample extension. Biomed","volume":"40","author":"Wei","year":"2018","journal-title":"Signal Proc. Control"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sergeeva, M., Ryabchikov, I., Glaznev, M., and Gusarova, N.F. (2016, January 18\u201322). Classification of pulmonary nodules on computed tomography scans. Evaluation of the effectiveness of application of textural features extracted using wavelet transform of image. Proceedings of the 2016 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology (FRUCT-ISPIT), St. Petersburg, Russia.","DOI":"10.1109\/FRUCT-ISPIT.2016.7561541"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nascimento, L.B., de Paiva, A., and Silva, A.C. (2012, January 13\u201320). Lung Nodules Classification in CT Images Using Shannon and Simpson Diversity Indices and SVM. Proceedings of the MLDM 2012: Machine Learning and Data Mining in Pattern Recognition, Berlin, Germany.","DOI":"10.1007\/978-3-642-31537-4_36"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"9370","DOI":"10.1038\/s41598-017-08764-7","article-title":"Diagnostic classification of solitary pulmonary nodules using dual time F-18-FDG PET\/CT image texture features in granuloma-endemic regions","volume":"7","author":"Song","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/JBHI.2017.2661805","article-title":"A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs","volume":"22","author":"Li","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Dandil, E., \u00c7akiroglu, M., Eksi, Z., Ozkan, M., Kurt, O.K., and Canan, A. (2014, January 11\u201314). Artificial neural network-based classification system for lung nodules on computed tomography scans. Proceedings of the 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Tunis, Tunisia.","DOI":"10.1109\/SOCPAR.2014.7008037"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Htwe, K.Z., Yamamori, K., Katayama, T., and Kyi, T.M. (2016, January 11\u201314). Automated lung nodule classification by artificial neural network and fuzzy inference system. Proceedings of the 2016 IEEE 5th Global Conference on Consumer Electronics (GCCE), Kyoto, Japan.","DOI":"10.1109\/GCCE.2016.7800447"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/S0895-6111(99)00033-6","article-title":"The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography","volume":"23","author":"Wyckoff","year":"1999","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_69","unstructured":"Narayanan, L.A., and Jeeva, J.B. (2015, January 9\u201310). A Computer Aided Diagnosis for detection and classification of lung nodules. Proceedings of the 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TBME.2013.2295593","article-title":"Lung Nodule Classification with Multilevel Patch-Based Context Analysis","volume":"61","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Song, Y., Cai, W.D., Wang, Y., and Feng, D.D. (2012, January 2\u20135). Location classification of lung nodules with optimized graph construction. Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain.","DOI":"10.1109\/ISBI.2012.6235841"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Farag, A., Elhabian, S., Graham, J., Farag, A., and Falk, R. (2010, January 20\u201324). Toward Precise Pulmonary Nodule Descriptors for Nodule Type Classification. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Beijing, China.","DOI":"10.1007\/978-3-642-15711-0_78"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1049\/iet-ipr.2016.1014","article-title":"Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm","volume":"12","author":"Li","year":"2018","journal-title":"IET Image Process."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s10916-017-0874-5","article-title":"Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric","volume":"42","author":"Wei","year":"2018","journal-title":"J. Med. Syst."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Sasidhar, B., Geetha, G., Khodanpur, B.I., and Babu, D.R.R. (2016, January 16\u201317). Automatic Classification of Lung Nodules into Benign or Malignant Using SVM Classifier. Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Bhubaneswar, India.","DOI":"10.1007\/978-981-10-3156-4_58"},{"key":"ref_76","unstructured":"Farag, A.A., Ali, A.M., Graham, J.H., Elhabian, S.Y., Farag, A.A., and Falk, R. (December, January 29). Feature-Based Lung Nodule Classification. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA."},{"key":"ref_77","unstructured":"Chen, H., Wu, W.F., Xia, H., Du, J., Yang, M., and Ma, B. (June, January 29). Classification of Pulmonary Nodules Using Neural Network Ensemble. Proceedings of the 8th International Conference on Advances in Neural Networks, Guilin, China."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Felix, A., Oliveira, M.C., Machado, A., and Ferreira, J.R. (2016, January 4\u20137). Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules. Proceedings of the 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Sao Paulo, Brazil.","DOI":"10.1109\/SIBGRAPI.2016.061"},{"key":"ref_79","unstructured":"Dilger, S., Judisch, A., and Hoffman, E.A. (2013). The Use of Surrounding Lung Parenchyma For The Automated Classification Of Pulmonary Nodules. Am. J. Respir. Crit. Care Med., 187."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Han, F.F., Zhang, G.P., Wang, H.F., Song, B.W., Lu, H.B., Zhao, D.Z., Zhao, H., and Liang, Z.R. (2013, January 19\u201320). A texture feature analysis for diagnosis of pulmonary nodules using LIDC-IDRI database. Proceedings of the 2013 IEEE International Conference on Medical Imaging Physics and Engineering, Shenyang, China.","DOI":"10.1109\/ICMIPE.2013.6864494"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"da Silva, C.A., Silva, A.C., Netto, S.M.B., Paiva, A.C., Junior, G.B., and Nunes, R.A. (2009, January 23\u201325). Lung Nodules Classification in CT Images Using Simpson\u2019s Index, Geometrical Measures and One-Class SVM. Proceedings of the MLDM 2009: Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany.","DOI":"10.1007\/978-3-642-03070-3_61"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1118\/1.2207129","article-title":"Computer-aided diagnosis of pulmonary nodules on CT scans: Segmentation and classification using 3D active contours","volume":"33","author":"Way","year":"2006","journal-title":"Med. Phys."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhu, W.T., Liu, C.C., Fan, W., and Xie, X.H. (2017). DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification. arXiv.","DOI":"10.1101\/189928"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2124","DOI":"10.1007\/s00330-017-5171-7","article-title":"Pulmonary subsolid nodules: Value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement","volume":"28","author":"Kim","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Jirapatnakul, A.C., Reeves, A.P., Apanasovich, T.V., Biancardi, A.M., Yankelevitz, D.F., and Henschke, C.I. (2007, January 12\u201315). Pulmonary Nodule Classification: Size Distribution Issues. Proceedings of the 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA.","DOI":"10.1109\/ISBI.2007.357085"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1166\/jmihi.2017.2259","article-title":"Automated Classification of Pulmonary Nodules for Lung Adenocarcinomas Risk Evaluation: An Effective CT Analysis by Clustering Density Distribution Algorithm","volume":"7","author":"Le","year":"2017","journal-title":"Med. Imaging Health Inform."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"210","DOI":"10.2174\/1573405613666161209105033","article-title":"A content-based image retrieval scheme for lung nodule classification","volume":"13","author":"Wei","year":"2017","journal-title":"Curr. Med. Imaging Rev."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1118\/1.1573210","article-title":"Automated lung nodule classification following automated nodule detection on CT: A serial approach","volume":"3","author":"Armato","year":"2003","journal-title":"Med. Phys."},{"key":"ref_89","first-page":"413","article-title":"Pulmonary nodule classification based on CT density distribution using 3-D thoracic CT images","volume":"5","author":"Kawata","year":"2004","journal-title":"Prog. Biomed. Opt. Imaging"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Kawata, Y., Niki, N., and Ohmatsu, H. (2002). Three-dimensional computer-aided diagnosis schemes for classification of benign and malignant pulmonary nodules. Comput. Assist. Radiol. Surg. Proc., 764\u2013769.","DOI":"10.1007\/978-3-642-56168-9_128"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Tartar, A., Akan, A., and Kilic, N. (2014, January 26\u201330). A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944661"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Kim, B.C., Sung, Y.S., and Suk, H.L. (2016, January 22\u201324). Deep feature learning for pulmonary nodule classification in a lung CT. Proceedings of the 2016 4th International Winter Conference on Brain-Computer Interface (BCI), Yongpyong, South Korea.","DOI":"10.1109\/IWW-BCI.2016.7457462"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Hancock, M.C., and Magnan, J.F. (2017, January 3). Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: An investigation using the Lung Image Database Consortium dataset. Proceedings of the Medical Imaging: Computer-Aided Diagnosis, Orlando, FL, USA.","DOI":"10.1117\/12.2254446"},{"key":"ref_94","unstructured":"Shewaye, T.N., and Mekonnen, A.A. (2016). Benign-Malignant Lung Nodule Classification with Geometric and Appearance Histogram Features. arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3078374","DOI":"10.1155\/2018\/3078374","article-title":"Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification","volume":"2018","author":"Mao","year":"2018","journal-title":"Complexity"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.cag.2017.07.020","article-title":"Hybrid-feature-guided lung nodule type classification on CT images","volume":"70","author":"Yuan","year":"2018","journal-title":"Comput. Graph."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/TMI.2014.2371821","article-title":"Bag-of-Frequencies: A Descriptor of Pulmonary Nodules in Computed Tomography Images","volume":"34","author":"Ciompi","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"El-Baz, A., Gimel\u2019farb, G.L., Falk, R., and El-Ghar, M.A. (2010, January 14\u201317). Appearance analysis for diagnosing malignant lung nodules. Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands.","DOI":"10.1109\/ISBI.2010.5490380"},{"key":"ref_99","first-page":"1091279","article-title":"Lung Nodule Image Classification Based on Local Difference Pattern and Combined Classifier. Comp","volume":"7","author":"Mao","year":"2016","journal-title":"Math. Methods Med."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Huang, P.W., Lin, P.L., Lee, C.H., and Lee, C.H. (2013, January 4\u20136). A Classification System of Lung Nodules in CT Images Based on Fractional Brownian Motion Model. Proceedings of the 2013 International Conference on System Science and Engineering (ICSSE), Budapest, Hungary.","DOI":"10.1109\/ICSSE.2013.6614710"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1016\/j.patcog.2013.06.017","article-title":"Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model","volume":"46","author":"Lin","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Zhang, F., Song, Y., Cai, W.D., Zhou, Y., Shan, S., and Feng, D. (2013, January 26\u201328). Context Curves for Classification of Lung Nodule Images. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, TAS, Australia.","DOI":"10.1109\/DICTA.2013.6691494"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1007\/s11548-017-1605-6","article-title":"Pulmonary nodule classification with deep residual networks","volume":"12","author":"Nibali","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/42.746620","article-title":"Computer-Aided Diagnosis: A Neural Network Based Approach to Lung Nodule Detection","volume":"17","author":"Penedo","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","article-title":"Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection","volume":"64","author":"Dou","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Jia, T., Bai, Y.K., Zhang, H., Chen, D.Y., Yu, X.S., and Wu, C.D. (2016, January 28\u201330). Lung Nodules Classification Based on Growth Changes and Registration Technology. Proceedings of the 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, China.","DOI":"10.1109\/CCDC.2016.7531956"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1166\/jmihi.2016.1871","article-title":"Lung Nodule Image Classification Based on Ensemble Machine Learning","volume":"6","author":"Mao","year":"2016","journal-title":"Med. Imaging Health Inform."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11548-017-1696-0","article-title":"Agile convolutional neural network for pulmonary nodule classification using CT images","volume":"13","author":"Zhao","year":"2018","journal-title":"Comput. Assist. Radiol. Surg."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/JBHI.2017.2725903","article-title":"An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network","volume":"22","author":"Jiang","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Shen, W., Zhou, M., Yang, F., Yang, C.Y., and Tian, J. (2015). Multi-scale Convolutional Neural Networks for Lung Nodule Classification. Information Processing in Medical Imaging, Springer.","DOI":"10.1007\/978-3-319-19992-4_46"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Jin, X.Y., Ma, C.H., Zhang, Y.C., and Li, L.J. (2017, January 9\u201310). Classification of Lung Nodules Based on Convolutional Deep Belief Network. Proceedings of the 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2017.57"},{"key":"ref_112","first-page":"8314740","article-title":"Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images","volume":"2017","author":"Song","year":"2017","journal-title":"Healthc. Eng."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"19039","DOI":"10.1007\/s11042-017-4480-9","article-title":"Lung nodules diagnosis based on evolutionary convolutional neural network","volume":"76","author":"Silva","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/ima.22206","article-title":"Multiview convolutional neural networks for lung nodule classification","volume":"27","author":"Liu","year":"2017","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Xu, Y.X., Zhang, G.K., Li, Y., Luo, Y., and Lu, J.W. (2017, January 14\u201318). A Hybrid Model: DGnet-SVM for the Classification of Pulmonary Nodules. Proceedings of the International Conference on Neural Information Processing, Guangzhou, China.","DOI":"10.1007\/978-3-319-70093-9_78"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1166\/jmihi.2015.1673","article-title":"Benign and Malignant Lung Nodule Classification Based on Deep Learning Feature","volume":"5","author":"Jia","year":"2015","journal-title":"Med. Imaging Health Inform."},{"key":"ref_117","unstructured":"Thammasorn, P., Wu, W., Pierce, L.A., Pipavath, S.N., Lampe, P.D., Houghton, A.M., Haynor, D.R., Chaovalitwongse, W.A., and Kinahan, P.E. (2018, January 27). Deep-learning derived features for lung nodule classification with limited datasets. Proceedings of the Medical Imaging: Computer-Aided Diagnosis, Houston, TX, USA."},{"key":"ref_118","first-page":"2015","article-title":"Computer-aided classification of lung nodules on computed tomography images via deep learning technique","volume":"8","author":"Hua","year":"2015","journal-title":"OncoTargets Ther."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Kumar, D., Wong, A., and Clausi, D.A. (2015, January 3\u20135). Lung Nodule Classification Using Deep Features in CT Images. Proceedings of the 2015 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, Canada.","DOI":"10.1109\/CRV.2015.25"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sun, W.Q., Zheng, B., and Qian, W. (2016). Computer aided lung cancer diagnosis with deep learning algorithms. SPIE, 9785.","DOI":"10.1117\/12.2216307"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.patcog.2016.09.029","article-title":"Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs CNNs","volume":"63","author":"Tajbakhsh","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/TMI.2005.852048","article-title":"Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network","volume":"24","author":"Suzuki","year":"2005","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_123","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_124","doi-asserted-by":"crossref","unstructured":"Dey, R., Lu, Z.J., and Hong, Y. (2018, January 4\u20137). Diagnostic Classification of Lung Nodules Using 3D Neural Networks. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"9286","DOI":"10.1038\/s41598-018-27569-w","article-title":"Highly accurate model for prediction of lung nodule malignancy with CT scans","volume":"8","author":"Causey","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Zhu, W.T., Liu, C.C., Fan, W., and Xie, X.H. (2018, January 12\u201315). DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00079"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Kang, G.X., Liu, K., Hou, B.B., and Zhang, N. (2017). 3D multi-view convolutional neural networks for lung nodule classification. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0188290"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Yan, X.J., Pang, J.N., Qi, H., Zhu, Y.X., Bai, C.X., Geng, X., Liu, M.N., Terzopoulos, D., and Ding, X.W. (2016). Classification of Lung Nodule Malignancy Risk on Computed Tomography Images Using Convolutional Neural Network:A Comparison Between 2D and 3D Strategies. Computer Vision\u2014ACCV 2016 Workshops, Springer.","DOI":"10.1007\/978-3-319-54526-4_7"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","article-title":"Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks","volume":"35","author":"Setio","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Paing, M.P., and Choomchuay, S. (September, January 31). Classification of Margin Characteristics from 3D Pulmonary Nodules. Proceedings of the 2017 10th Biomedical Engineering International Conference, Hokkaido, Japan.","DOI":"10.1109\/BMEiCON.2017.8229104"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1118\/1.4955794","article-title":"Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression","volume":"43","author":"Huang","year":"2016","journal-title":"Med. Phys."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Zhang, F., Song, Y., Cai, W.D., Zhou, Y., Fulham, M.J., Eberl, S., Shan, S., and Feng, D. (May, January 29). A ranking-based lung nodule image classification method using unlabeled image knowledge. Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, China.","DOI":"10.1109\/ISBI.2014.6868129"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Antonelli, M., Cococcioni, M., Lazzerini, B., Marcelloni, F., and Stefanescu, D.C. (2008, January 17\u201319). A Multi-Classifier System for Pulmonary Nodule Classification. Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS), Jyvaskyla, Finland.","DOI":"10.1109\/CBMS.2008.70"},{"key":"ref_134","unstructured":"Dilger, S.K.N., Judisch, A., Uthoff, J., Hoffman, E.A., Newell, J.D., and Sieren, J.C. (2014). A Systematic Investigation into Lung Tissue Feature Extraction to Improve The Classification Of Pulmonary Nodules. Am. J. Respir. Crit. Care Med., 189."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.infrared.2017.12.015","article-title":"Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine","volume":"89","author":"Leksir","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.bbe.2015.12.005","article-title":"Recognition of images of finger skin with application of histogram, image filtration and K-NN classifier","volume":"36","author":"Glowacz","year":"2016","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.neucom.2018.09.002","article-title":"Structured random forest for label distribution learning","volume":"320","author":"Chen","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s12938-016-0243-5","article-title":"Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks","volume":"15","author":"Koprowski","year":"2016","journal-title":"Biomed. Eng. Online"},{"key":"ref_139","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_140","unstructured":"Zhang, C.Y., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (arXiv, 2017). Understanding deep learning requires rethinking generalization, arXiv."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:24:06Z","timestamp":1760185446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,7]]},"references-count":141,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19010194"],"URL":"https:\/\/doi.org\/10.3390\/s19010194","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,7]]}}}