{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:50:10Z","timestamp":1775584210374,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"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>Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib\/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.<\/jats:p>","DOI":"10.3390\/s23146585","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T03:03:25Z","timestamp":1690167805000},"page":"6585","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6691-9739","authenticated-orcid":false,"given":"Michael J.","family":"Horry","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"IBM Australia Limited, Sydney, NSW 2000, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-5424","authenticated-orcid":false,"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-5056","authenticated-orcid":false,"given":"Manoranjan","family":"Paul","sequence":"additional","affiliation":[{"name":"Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Westmead Hospital, Westmead, NSW 2145, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui Wen","family":"Loh","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5117-8333","authenticated-orcid":false,"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia"},{"name":"Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia"},{"name":"School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"key":"ref_1","unstructured":"(2021, November 02). WHO Fact Sheet\u2014Cancer. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"170070","DOI":"10.1098\/rsob.170070","article-title":"Progress and prospects of early detection in lung cancer","volume":"7","author":"Crosbie","year":"2017","journal-title":"Open Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ejca.2020.04.035","article-title":"Systematic review and meta-analysis on the impact of lung cancer screening by low-dose computed tomography","volume":"134","author":"Sadate","year":"2020","journal-title":"Eur. J. Cancer"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1038\/s43018-020-00142-z","article-title":"The challenges of implementing low-dose computed tomography for lung cancer screening in low- and middle-income countries","volume":"1","author":"Guerra","year":"2020","journal-title":"Nat. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA A Cancer J. Clin."},{"key":"ref_7","first-page":"e589","article-title":"To Screen or not to Screen: Low Dose Computed Tomography in Comparison to Chest Radiography or Usual Care in Reducing Morbidity and Mortality from Lung Cancer","volume":"8","author":"Dajac","year":"2016","journal-title":"Cureus"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"234","DOI":"10.21037\/tlcr.2018.05.12","article-title":"Lung cancer screening with low-dose CT: A world-wide view","volume":"7","author":"Pinsky","year":"2018","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"S106","DOI":"10.21037\/tlcr.2019.03.03","article-title":"Feasibility of lung cancer screening in developing countries: Challenges, opportunities and way forward","volume":"8","author":"Shankar","year":"2019","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_10","unstructured":"(2021, November 15). WHO Portable Digital Radiography System: Technical Specifications. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240033818."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cavoukian, A. (2012). Privacy by Design, Information and Privacy Commissioner of Ontario.","DOI":"10.4018\/978-1-61350-501-4.ch007"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wahlstrom, K., Ul-Haq, A., and Burmeister, O. (2020). Privacy by design. Australas. J. Inf. Syst., 24.","DOI":"10.3127\/ajis.v24i0.2801"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104468","DOI":"10.1016\/j.engappai.2021.104468","article-title":"Achieving security and privacy in federated learning systems: Survey, research challenges and future directions","volume":"106","author":"Flanagan","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64279","DOI":"10.1109\/ACCESS.2019.2916849","article-title":"A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases","volume":"7","author":"Allaouzi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100391","DOI":"10.1016\/j.imu.2020.100391","article-title":"Hybrid deep learning for detecting lung diseases from X-ray images","volume":"20","author":"Bharati","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_16","first-page":"590","article-title":"CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison","volume":"33","author":"Irvin","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.neucom.2020.03.127","article-title":"Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels","volume":"437","author":"Pham","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101554","DOI":"10.1016\/j.bspc.2019.04.031","article-title":"DualCheXNet: Dual asymmetric feature learning for thoracic disease classification in chest X-rays","volume":"53","author":"Chen","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","unstructured":"Mori, K., and Hahn, H.K. (2019). Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline, SPIE. Medical Imaging 2019: Computer-Aided Diagnosis."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ho, T., and Gwak, J. (2019). Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography. Appl. Sci., 9.","DOI":"10.3390\/app9194130"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6381","DOI":"10.1038\/s41598-019-42294-8","article-title":"Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification","volume":"9","author":"Ivo","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101846","DOI":"10.1016\/j.media.2020.101846","article-title":"Triple attention learning for classification of 14 thoracic diseases using chest radiography","volume":"67","author":"Wang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compmedimag.2019.05.005","article-title":"SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images","volume":"75","author":"Liu","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1111\/coin.12241","article-title":"Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks","volume":"36","author":"Mendoza","year":"2020","journal-title":"Comput. Intell."},{"key":"ref_26","unstructured":"Simk\u00f3, G., Orb\u00e1n, G., M\u00e1day, P., and Horv\u00e1th, G. (2008, January 23\u201327). Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs. Proceedings of the 4th European Conference of the International Federation for Medical and Biological Engineering, ECIFMBE 2008, Antwerp, Belgium."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Orb\u00e1n, G., Horv\u00e1th, \u00c1., and Horv\u00e1th, G. (2010, January 27\u201330). Lung nodule detection on Rib eliminated radiographs. Proceedings of the 12th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2010, Chalkidiki, Greece.","DOI":"10.1007\/978-3-642-13039-7_91"},{"key":"ref_28","first-page":"19","article-title":"An X-ray CAD system with ribcage suppression for improved detection of lung lesions","volume":"57","year":"2013","journal-title":"Period. Polytech. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gang, P., Zhen, W., Zeng, W., Gordienko, Y., Kochura, Y., Alienin, O., Rokovyi, O., and Stirenko, S. (2018, January 29\u201331). Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer. Proceedings of the 2018 tenth international conference on advanced computational intelligence (ICACI), Xiamen, China.","DOI":"10.1109\/ICACI.2018.8377579"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Robinson, C., Trivedi, A., Blazes, M., Ortiz, A., Desbiens, J., Gupta, S., Dodhia, R., Bhatraju, P.K., Liles, W.C., and Lee, A. (2021). Deep learning models for COVID-19 chest X-ray classification: Preventing shortcut learning using feature disentanglement. medRxiv, Preprint.","DOI":"10.1101\/2021.02.11.20196766"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6010","DOI":"10.1038\/s41467-020-19784-9","article-title":"Training confounder-free deep learning models for medical applications","volume":"11","author":"Zhao","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_32","unstructured":"Creager, E., Madras, D., Jacobsen, J.-H., Weis, M., Swersky, K., Pitassi, T., and Zemel, R. (2019, January 9\u201315). Flexibly fair representation learning by disentanglement. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Roy, P.C., and Boddeti, V.N. (2019, January 16\u201320). Mitigating information leakage in image representations: A maximum entropy approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00269"},{"key":"ref_34","first-page":"585","article-title":"Controllable Invariance through Adversarial Feature Learning","volume":"30","author":"Xie","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","article-title":"AI for radiographic COVID-19 detection selects shortcuts over signal","volume":"3","author":"DeGrave","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_36","first-page":"232","article-title":"CheXclusion: Fairness gaps in deep chest X-ray classifiers","volume":"26","author":"Liu","year":"2021","journal-title":"Pac. Symp. Biocomput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_38","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, Savannah, GA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_40","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","volume":"174","author":"Shiraishi","year":"2000","journal-title":"Am. J. Roentgenol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"251S","DOI":"10.1016\/S0197-2456(00)00097-0","article-title":"The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial of the National Cancer Institute: History, organization, and status","volume":"21","author":"Gohagan","year":"2000","journal-title":"Control. Clin. Trials"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1016\/j.media.2009.07.001","article-title":"A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification","volume":"13","author":"Murphy","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/42.993132","article-title":"Automatic detection of abnormalities in chest radiographs using local texture analysis","volume":"21","author":"Katsuragawa","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.media.2007.10.004","article-title":"Performance analysis of a new computer aided detection system for identifying lung nodules on chest radiographs","volume":"12","author":"Hardie","year":"2008","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TMI.2006.871549","article-title":"Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)","volume":"25","author":"Suzuki","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","article-title":"End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography","volume":"25","author":"Ardila","year":"2019","journal-title":"Nat. Med."},{"key":"ref_47","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":"Setio","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.media.2005.09.003","article-title":"A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database","volume":"10","author":"Schilham","year":"2006","journal-title":"Med. Image Anal."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.acra.2004.11.017","article-title":"False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network","volume":"12","author":"Suzuki","year":"2005","journal-title":"Acad. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1118\/1.598028","article-title":"Development of an improved CAD scheme for automated detection of lung nodules in digital chest images","volume":"24","author":"Xu","year":"1997","journal-title":"Med. Phys."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1118\/1.3611983","article-title":"SU-E-T-33: Pydicom: An open source DICOM library","volume":"38","author":"Mason","year":"2011","journal-title":"Med. Phys."},{"key":"ref_54","unstructured":"Murphy, A., and Jones, J. (2023, July 16). Radiographic Contrast. Available online: https:\/\/radiopaedia.org\/articles\/58718."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s10278-006-0623-7","article-title":"Study of automatic enhancement for chest radiograph","volume":"19","author":"Shuyue","year":"2006","journal-title":"J. Digit. Imaging"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-019-0105-1","article-title":"Deep learning predicts hip fracture using confounding patient and healthcare variables","volume":"2","author":"Badgeley","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_58","unstructured":"Rabinovich, A., Vedaldi, A., and Belongie, S. (2020). Does Image Segmentation Improve Object Categorization?, Cornell University."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Teixeira, L.O., Pereira, R.M., Bertolini, D., Oliveira, L.S., Nanni, L., Cavalcanti, G.D.C., and Costa, Y.M.G. (2021). Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images. Sensors, 21.","DOI":"10.3390\/s21217116"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11548-019-01917-1","article-title":"A review on lung boundary detection in chest X-rays","volume":"14","author":"Candemir","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_61","unstructured":"Hwang, S., and Park, S. (2017). Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs","volume":"37","author":"Novikov","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_63","first-page":"475","article-title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","volume":"4","author":"Jaeger","year":"2014","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_64","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L.V.D., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_66","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-Net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6625688","DOI":"10.1155\/2021\/6625688","article-title":"R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation","volume":"2021","author":"Zuo","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"153535","DOI":"10.1109\/ACCESS.2020.3017915","article-title":"Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks","volume":"8","author":"Munawar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","article-title":"An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks","volume":"177","author":"Souza","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_72","unstructured":"Horv\u00e1th, \u00c1. (2021). Bone Shadow Eliminated Images of the JSRT Database, Budapest University of Technology and Economics (BME)."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Gusarev, M., Kuleev, R., Khan, A., Rivera, A.R., and Khattak, A.M. (2017, January 23\u201325). Deep learning models for bone suppression in chest radiographs. Proceedings of the 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Manchester, UK.","DOI":"10.1109\/CIBCB.2017.8058543"},{"key":"ref_74","unstructured":"Flam, D. (2023, July 16). Available online: https:\/\/github.com\/danielnflam\/Deep-Learning-Models-for-bone-suppression-in-chest-radiographs."},{"key":"ref_75","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"149808","DOI":"10.1109\/ACCESS.2020.3016780","article-title":"COVID-19 Detection through Transfer Learning using Multimodal Imaging Data","volume":"8","author":"Horry","year":"2020","journal-title":"IEEE Access"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ausawalaithong, W., Thirach, A., Marukatat, S., and Wilaiprasitporn, T. (2018, January 21\u201324). Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach. Proceedings of the 11th Biomedical Engineering International Conference (BMEiCON), Chiang Mai, Thailand.","DOI":"10.1109\/BMEiCON.2018.8609997"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s11548-015-1278-y","article-title":"A novel bone suppression method that improves lung nodule detection","volume":"11","author":"Young","year":"2016","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"118","DOI":"10.5152\/dir.2016.16187","article-title":"Missed lung cancer: When, where, and why?","volume":"23","author":"Franchi","year":"2017","journal-title":"Diagn. Interv. Radiol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6585\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:16:43Z","timestamp":1760127403000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,21]]},"references-count":80,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146585"],"URL":"https:\/\/doi.org\/10.3390\/s23146585","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,21]]}}}