{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T01:50:37Z","timestamp":1791337837590,"version":"4.3.1"},"reference-count":106,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Lung cancer has one of the highest mortality rates of all cancers and poses a severe threat to people\u2019s health. Therefore, diagnosing lung nodules at an early stage is crucial to improving patient survival rates. Numerous computer-aided diagnosis (CAD) systems have been developed to detect and classify such nodules in their early stages. Currently, CAD systems for pulmonary nodules comprise data acquisition, pre-processing, lung segmentation, nodule detection, false-positive reduction, segmentation, and classification. A number of review articles have considered various components of such systems, but this review focuses on segmentation and classification parts. Specifically, categorizing segmentation parts based on lung nodule type and network architectures, i.e., general neural network and multiview convolution neural network (CNN) architecture. Moreover, this work organizes related literature for classification of parts based on nodule or non-nodule and benign or malignant. The essential CT lung datasets and evaluation metrics used in the detection and diagnosis of lung nodules have been systematically summarized as well. Thus, this review provides a baseline understanding of the topic for interested readers.<\/jats:p>","DOI":"10.3390\/diagnostics12020298","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:38:58Z","timestamp":1643143138000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":114,"title":["Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuda","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongzhi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haseeb","family":"Hassan","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"},{"name":"Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen 518060, China"},{"name":"College of Applied Sciences, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4748-2882","authenticated-orcid":false,"given":"Bingding","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1148\/radiol.2017162894","article-title":"Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society","volume":"285","author":"Bankier","year":"2017","journal-title":"Radiology"},{"key":"ref_2","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_3","unstructured":"(2022, January 21). ELCAP Public Lung Image Database. Available online: http:\/\/www.via.cornell.edu\/lungdb.html."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cmpb.2013.08.015","article-title":"Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor","volume":"113","author":"Choi","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1148\/radiol.2432052052","article-title":"Pulmonary Nodules: Sensitivity of Maximum Intensity Projection versus That of Volume Rendering of 3D Multidetector CT Data","volume":"243","author":"Peloschek","year":"2007","journal-title":"Radiology"},{"key":"ref_6","first-page":"47","article-title":"Pulmonary subsolid nodules: What radiologists need to know about the imaging features and management strategy","volume":"20","author":"Kim","year":"2013","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_7","unstructured":"(2022, January 21). Radiopedia, Pulmonary Nodule. Available online: https:\/\/radiopaedia.org\/articles\/pulmonary-nodule-1."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1148\/radiol.2312030167","article-title":"Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable?","volume":"231","author":"Revel","year":"2004","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"52","DOI":"10.21037\/tlcr.2017.01.05","article-title":"Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening","volume":"6","author":"Han","year":"2017","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1102\/1470-7330.2005.0018","article-title":"Computer aided detection (CAD): An overview","volume":"5","author":"Castellino","year":"2005","journal-title":"Cancer Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1148\/radiol.2392050631","article-title":"Distinguishing benign from malignant pulmonary nodules with helical chest CT in children with malignant solid tumors","volume":"239","author":"McCarville","year":"2006","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1148\/radiol.10081308","article-title":"Computer-aided Classification of Breast Masses: Performance and Interobserver Variability of Expert Radiologists versus Residents","volume":"258","author":"Singh","year":"2011","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1146\/annurev-bioeng-071812-152416","article-title":"Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer","volume":"15","author":"Giger","year":"2013","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/TMI.2004.834617","article-title":"Computer-aided diagnosis of solid breast nodules: Use of an artificial neural network based on multiple sonographic features","volume":"23","author":"Joo","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1118\/1.596478","article-title":"Computerized detection of pulmonary nodules in digital chest images: Use of morphological filters in reducing false-positive detections","volume":"17","author":"Giger","year":"1990","journal-title":"Med. Phys."},{"key":"ref_19","unstructured":"Ying, W., Cunxi, C., Tong, J., and Xinhe, X. (2009, January 17\u201319). Segmentation of regions of interest in lung CT images based on 2-D OTSU optimized by genetic algorithm. Proceedings of the 2009 Chinese Control and Decision Conference, Guilin, China."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Helen, R., Kamaraj, N., Selvi, K., and Raman, V.R. (2011, January 23\u201324). Segmentation of pulmonary parenchyma in CT lung images based on 2D Otsu optimized by PSO. Proceedings of the International Conference on Emerging Trends in Electrical and Computer Technology, Nagercoil, India.","DOI":"10.1109\/ICETECT.2011.5760176"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Z., Guo, M., and Li, P. (2014, January 27\u201330). Hidden conditional random field for lung nodule detection. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025714"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.protcy.2016.05.209","article-title":"Multilevel Thresholding Based Segmentation and Feature Extraction for Pulmonary Nodule Detection","volume":"24","author":"John","year":"2016","journal-title":"Procedia Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.1118\/1.4948498","article-title":"Automated detection of pulmonary nodules in PET\/CT images: Ensemble false-positive reduction using a convolutional neural network technique","volume":"43","author":"Teramoto","year":"2016","journal-title":"Med. Phys."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mastouri, R., Neji, H., Hantous-Zannad, S., and Khlifa, N. (2018, January 28\u201330). A morphological operation-based approach for Sub-pleural lung nodule detection from CT images. Proceedings of the 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia.","DOI":"10.1109\/MECBME.2018.8402411"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.engappai.2014.07.007","article-title":"Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM","volume":"36","author":"Santos","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s12938-015-0003-y","article-title":"Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine","volume":"14","author":"Orozco","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5042","DOI":"10.1118\/1.4927573","article-title":"Hybrid detection of lung nodules on CT scan images","volume":"42","author":"Lu","year":"2015","journal-title":"Med. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Farahani, F.V., Ahmadi, A., and Zarandi, M.F. (2015, January 12\u201315). Lung nodule diagnosis from CT images based on ensemble learning. Proceedings of the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Niagara Falls, ON, Canada.","DOI":"10.1109\/CIBCB.2015.7300281"},{"key":"ref_29","unstructured":"Klik MA, J., v Rikxoort, E.M., Peters, J.F., Gietema, H.A., Prokop, M., and v Ginneken, B. Improved classification of pulmonary nodules by automated detection of benign subpleural lymph nodes. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.eswa.2016.10.039","article-title":"Lung nodule classification using artificial crawlers, directional texture and support vector machine","volume":"69","author":"Froz","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21037\/jmai.2019.04.01","article-title":"A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques","volume":"2","author":"Wu","year":"2019","journal-title":"J. Med. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e180084","DOI":"10.1148\/ryai.2019180084","article-title":"Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance","volume":"1","author":"Liu","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shen, W., Zhou, M., Yang, F., Yang, C., and Tian, J. (July, January 28). Multiscale convolutional neural networks for lung nodule classification. Proceedings of the International Conference on Information Processing in Medical Imaging 2014, Isle of Skye, UK.","DOI":"10.1007\/978-3-319-19992-4_46"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"46479","DOI":"10.1038\/srep46479","article-title":"Towards automatic pulmonary nodule management in lung cancer screening with deep learning","volume":"7","author":"Ciompi","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"265","DOI":"10.4103\/0256-4602.101306","article-title":"Computer-aided detection and analysis of pulmonary nodule from CT images: A survey","volume":"29","author":"Dhara","year":"2012","journal-title":"IETE Tech. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1109\/TMI.2005.862753","article-title":"Computer analysis of computed tomography scans of the lung: A survey","volume":"25","author":"Sluimer","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_39","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: A survey","volume":"124","author":"Valente","year":"2015","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s10278-020-00320-6","article-title":"Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: A Comprehensive Review","volume":"33","author":"Halder","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.compbiomed.2018.10.033","article-title":"Automatic nodule detection for lung cancer in CT images: A review","volume":"103","author":"Zhang","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104806","DOI":"10.1016\/j.compbiomed.2021.104806","article-title":"A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning","volume":"137","author":"Gu","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"78075","DOI":"10.1109\/ACCESS.2019.2920980","article-title":"Detection and classification of pulmonary nodules using convolutional neural networks: A survey","volume":"7","author":"Monkam","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"El-Regaily, S.A., Salem MA, M., Aziz MH, A., and Roushdy, M.I. (2017, January 5\u20137). Lung nodule segmentation and detection in computed tomography. Proceedings of the 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/INTELCIS.2017.8260029"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1016\/j.aej.2016.06.002","article-title":"Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering","volume":"55","author":"Nithila","year":"2016","journal-title":"Alex. Eng. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1148\/rg.2015140232","article-title":"Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends","volume":"35","author":"Mansoor","year":"2015","journal-title":"RadioGraphics"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.bspc.2018.01.011","article-title":"Pulmonary nodule detection in medical images: A survey","volume":"43","author":"Zhang","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","article-title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge","volume":"42","author":"Traverso","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_50","first-page":"S389","article-title":"Treatment of non-small cell lung cancer (NSCLC)","volume":"5","author":"Zarogoulidis","year":"2013","journal-title":"J. Thorac. Dis."},{"key":"ref_51","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":"Armato","year":"2010","journal-title":"Med. Image Anal."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3390\/ai1010003","article-title":"Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans","volume":"1","author":"Riquelme","year":"2020","journal-title":"AI"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, B., Zhou, Z., Wang, J., and Wang, Y. (2018, January 4\u20137). Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363765"},{"key":"ref_54","unstructured":"Keetha, N.V., and Annavarapu, C.S.R. (2020). U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kumar, S., and Raman, S. (2020). Lung Nodule Segmentation Using 3-Dimensional Convolutional Neural Networks[M]\/\/Soft Computing for Problem Solving, Springer.","DOI":"10.1007\/978-981-15-0035-0_48"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1038\/s41598-020-69817-y","article-title":"Volumetric lung nodule segmentation using adaptive ROI with multi-view residual learning","volume":"10","author":"Usman","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compmedimag.2019.02.003","article-title":"Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks","volume":"74","author":"Huang","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1038\/s41598-019-48004-8","article-title":"iW-Net: An automatic and minimalistic interactive lung nodule segmentation deep network","volume":"9","author":"Aresta","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.ijleo.2018.08.086","article-title":"Improved U-NET network for pulmonary nodules segmentation","volume":"174","author":"Tong","year":"2018","journal-title":"Optik"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhao, C., Han, J., Jia, Y., and Gou, F. (2015, January 12\u201315). Lung nodule detection via 3D U-Net and contextual convolutional neural network. Proceedings of the 2018 International Conference on Networking and Network Applications (NaNA), Xi\u2019an, China.","DOI":"10.29007\/bgkd"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_62","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (October, January 27). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_63","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., and Ozair, S. (2014). Generative adversarial nets. Adv. Neural Inf. Process. Syst., 27, Available online: https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmen-tation. Proceedings of the 2016 4th International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"105792","DOI":"10.1016\/j.cmpb.2020.105792","article-title":"CoLe-CNN: Context-learning convolutional neural network with adaptive loss func-tion for lung nodule segmentation","volume":"198","author":"Pezzano","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhou, M., Gevaert, O., Tang, Z., Dong, D., Liu, Z., and Jie, T. (2017, January 11\u201315). A multi-view deep convolutional neural networks for lung nodule segmentation. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Glasgow, Scotland.","DOI":"10.1109\/EMBC.2017.8037182"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40644-020-00331-0","article-title":"Multi-view secondary input collaborative deep learning for lung nodule 3D segmentation","volume":"20","author":"Dong","year":"2020","journal-title":"Cancer Imaging"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1038\/s41551-020-00633-5","article-title":"Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning","volume":"4","author":"Ning","year":"2020","journal-title":"Nat. Biomed. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.media.2017.06.014","article-title":"Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation","volume":"40","author":"Wang","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2018.08.022","article-title":"NODULe: Combining constrained multi-scale LoG filters with densely dilated 3D deep convolutional neural network for pulmonary nodule detection","volume":"317","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105934","DOI":"10.1016\/j.asoc.2019.105934","article-title":"Dual-branch residual network for lung nodule segmentation","volume":"86","author":"Cao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s11548-019-01981-7","article-title":"Lung nodule classification using deep local\u2013global networks","volume":"14","author":"Lan","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Tang, H., Zhang, C., and Xie, X. (2019, January 13\u201317). Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32226-7_30"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Hesamian, M.H., Jia, W., He, X., and Kennedy, P.J. (2019, January 12\u201317). Atrous convolution for binary semantic segmentation of lung nodule. Proceedings of the ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682220"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, M., Dong, J., Dong, X., Yu, H., and Qi, L. (2018, January 19\u201321). Segmentation of lung nodule in CT images based on mask R-CNN. Proceedings of the 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka, Japan.","DOI":"10.1109\/ICAwST.2018.8517248"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Khosravan, N., and Bagci, U. (2018, January 18\u201321). Semi-supervised multi-task learning for lung cancer diagnosis. Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512294"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"8975078","DOI":"10.1155\/2020\/8975078","article-title":"Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning","volume":"2020","author":"Wu","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"5156416","DOI":"10.1155\/2019\/5156416","article-title":"Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss","volume":"2019","author":"Tran","year":"2019","journal-title":"J. Health Eng."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"6215085","DOI":"10.1155\/2016\/6215085","article-title":"Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images","volume":"2016","author":"Li","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s11548-020-02283-z","article-title":"A bilinear convolutional neural network for lung nodules classification on CT images","volume":"16","author":"Mastouri","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Bhavanishankar, K., and Sudhamani, M.V. (2019, January 25\u201326). Classification of Lung Nodules into Benign or Malignant and Development of a CBIRSystem for Lung CT Scans. Proceedings of the International Conference on Computational Vision and Bio Inspired Computing, Coimbatore, India.","DOI":"10.1007\/978-3-030-37218-7_64"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"175859","DOI":"10.1109\/ACCESS.2020.3026080","article-title":"Efficient lung nodule classification using transferable texture convolutional neural network","volume":"8","author":"Ali","year":"2020","journal-title":"IEEE Access"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"178827","DOI":"10.1109\/ACCESS.2019.2958663","article-title":"Gated-Dilated Networks for Lung Nodule Classification in CT Scans","volume":"7","author":"Lee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_85","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 2018 IEEE 15th interna-tional symposium on biomedical imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"ref_86","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_87","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_88","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, Halifax, NS, Canada.","DOI":"10.1109\/CRV.2015.25"},{"key":"ref_89","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":"J. Med. Imaging Health Inform."},{"key":"ref_90","unstructured":"Sang, J., Alam, M.S., and Xiang, H. (2019, January 15\u201316). Automated detection and classification for early stage lung cancer on CT images using deep learning. Proceedings of the Pattern Recognition and Tracking XXX. International Society for Optics and Photonics, Baltimore, MA, USA."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wang, W., Li, X., Yang, J., and Lu, T. (2018). Mixed link networks. arXiv.","DOI":"10.24963\/ijcai.2018\/391"},{"key":"ref_92","unstructured":"Agnes, S.A., and Anitha, J. (2020, January 8\u201310). Automatic 2D Lung Nodule Patch Classification using Deep Neural Networks. Proceedings of the 2020 Fourth International Conference on Inventive Systems and Control (ICISC), Coimbatore, India."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.eswa.2019.01.048","article-title":"An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification","volume":"128","author":"Shen","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_94","first-page":"012155","article-title":"Lung Nodule Classification in CT Images Using 3D DenseNet","volume":"1827","author":"Zhang","year":"2021","journal-title":"IOP Publishing"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1007\/s11547-019-01130-9","article-title":"Classification of lung nodules based on CT images using squeeze-and-excitation net-work and aggregated residual transformations","volume":"125","author":"Zhang","year":"2020","journal-title":"La Radiol. Med."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"An, Y., Hu, T., Wang, J., Lyu, J., Banerjee, S., and Ling, S.H. (2019, January 23\u201327). Lung Nodule Classification using A Novel Two-stage Convolutional Neural Networks Structure. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857744"},{"key":"ref_97","first-page":"214","article-title":"Classification of multi-scale lung nodules based on synchronized deep supervision","volume":"36","author":"Zhang","year":"2019","journal-title":"Comput. Appl. Softw."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1242","DOI":"10.1007\/s10278-020-00372-8","article-title":"Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification","volume":"33","author":"Liu","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Xia, K., Chi, J., Gao, Y., Jiang, Y., and Wu, C. (2021). Adaptive Aggregated Attention Network for Pulmonary Nodule Classification. Appl. Sci., 11.","DOI":"10.3390\/app11020610"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"S867","DOI":"10.21037\/jtd.2018.02.57","article-title":"Deep learning aided decision support for pulmonary nodules diagnosing: A review","volume":"10","author":"Yang","year":"2018","journal-title":"J. Thorac. Dis."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_103","unstructured":"Baldi, P. (2012, January 27). Autoencoders, unsupervised learning, and deep architectures. Proceedings of the ICML Workshop on Unsupervised and Transfer Learning, Bellevue, DC, USA."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Navamani, T.M. (2019). Efficient deep learning approaches for health informatics. Deep Learning and Parallel Computing Environment for Bioengineering Systems, Elsevier.","DOI":"10.1016\/B978-0-12-816718-2.00014-2"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"517","DOI":"10.3389\/fpubh.2021.671070","article-title":"Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets","volume":"9","author":"Liang","year":"2021","journal-title":"Front. Public Health"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"226","DOI":"10.4103\/atm.ATM_110_19","article-title":"Lung nodules: A comprehensive review on current approach and management","volume":"14","author":"Gaga","year":"2019","journal-title":"Ann. Thorac. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/2\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:07:17Z","timestamp":1760134037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/2\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":106,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["diagnostics12020298"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12020298","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]}}}