{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:47:17Z","timestamp":1783079237854,"version":"3.54.6"},"reference-count":151,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006242","name":"Universiti Malaysia Sabah","doi-asserted-by":"publisher","award":["SDK0191-2020"],"award-info":[{"award-number":["SDK0191-2020"]}],"id":[{"id":"10.13039\/501100006242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.<\/jats:p>","DOI":"10.3390\/jimaging6120131","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T20:06:09Z","timestamp":1606853169000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":153,"title":["A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4629-334X","authenticated-orcid":false,"given":"Stefanus Tao Hwa","family":"Kieu","sequence":"first","affiliation":[{"name":"Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4054-3064","authenticated-orcid":false,"given":"Abdullah","family":"Bade","sequence":"additional","affiliation":[{"name":"Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0431-8967","authenticated-orcid":false,"given":"Mohd Hanafi Ahmad","family":"Hijazi","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5460-5679","authenticated-orcid":false,"given":"Hoshang","family":"Kolivand","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,1]]},"reference":[{"key":"ref_1","unstructured":"Bousquet, J. (2007). Global Surveillance, Prevention and Control of Chronic Respiratory Diseases, World Health Organization."},{"key":"ref_2","unstructured":"Forum of International Respiratory Societies (2017). The Global Impact of Respiratory Disease, European Respiratory Society. [2nd ed.]."},{"key":"ref_3","unstructured":"World Health Organization (2020). Coronavirus Disease 2019 (COVID-19) Situation Report, World Health Organization. Technical Report March."},{"key":"ref_4","first-page":"821","article-title":"Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches","volume":"28","author":"Rahaman","year":"2020","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_5","first-page":"4208","article-title":"A new method of automatic recognition for tuberculosis disease diagnosis using support vector machines","volume":"28","author":"Yahiaoui","year":"2017","journal-title":"Biomed. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.patcog.2018.05.014","article-title":"Deep learning for image-based cancer detection and diagnosis-A survey","volume":"83","author":"Hu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"American Thoracic Society (2000). Diagnostic Standards and Classification of Tuberculosis in Adults and Children. Am. J. Respir. Crit. Care Med., 161, 1376\u20131395.","DOI":"10.1164\/ajrccm.161.4.16141"},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep Learning in Medical Image Analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20021","DOI":"10.1109\/ACCESS.2018.2823979","article-title":"A Greedy Deep Learning Method for Medical Disease Analysis","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1007\/s11684-019-0726-4","article-title":"Survey on deep learning for pulmonary medical imaging","volume":"14","author":"Ma","year":"2019","journal-title":"Front. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rajaraman, S., Candemir, S., Xue, Z., Alderson, P.O., Kohli, M., Abuya, J., Thoma, G.R., Antani, S., and Member, S. (2018, January 17\u201321). A novel stacked generalization of models for improved TB detection in chest radiographs. Proceedings of the 2018 40th Annual International Conference the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512337"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Gordienko, Y., Gang, P., Hui, J., Zeng, W., Kochura, Y., Alienin, O., Rokovyi, O., and Stirenko, S. (2019). Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer. Adv. Intell. Syst. Comput., 638\u2013647.","DOI":"10.1007\/978-3-319-91008-6_63"},{"key":"ref_15","first-page":"429","article-title":"Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images","volume":"8","author":"Kieu","year":"2019","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. Int. Workshop Mult. Classif. Syst., 1\u201315.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_17","unstructured":"Webb, A. (2003). Introduction To Biomedical Imaging, John Wiley & Sons, Inc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1136\/bmj.38977.669769.2C","article-title":"X ray imaging goes digital","volume":"333","year":"2006","journal-title":"Br. Med J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compbiomed.2017.08.001","article-title":"Pre-trained convolutional neural networks as feature extractors for tuberculosis detection","volume":"89","author":"Lopes","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ayan, E., and \u00dcnver, H.M. (2019). Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning. Sci. Meet. Electr.-Electron. Biomed. Eng. Comput. Sci, 1\u20135.","DOI":"10.1109\/EBBT.2019.8741582"},{"key":"ref_21","first-page":"18","article-title":"COVID-19 Detection using Artificial Intelligence","volume":"4","author":"Salman","year":"2020","journal-title":"Int. J. Acad. Eng. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Herman, G.T. (2009). Fundamentals of Computerized Tomography, Springer.","DOI":"10.1007\/978-1-84628-723-7"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Song, Q.Z., Zhao, L., Luo, X.K., and Dou, X.C. (2017). Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images. J. Healthc. Eng., 2017.","DOI":"10.1155\/2017\/8314740"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neucom.2018.12.086","article-title":"Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture","volume":"392","author":"Gao","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rao, P., Pereira, N.A., and Srinivasan, R. (2016, January 14\u201317). Convolutional neural networks for lung cancer screening in computed tomography (CT) scans. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics, IC3I 2016, Noida, India.","DOI":"10.1109\/IC3I.2016.7918014"},{"key":"ref_26","unstructured":"Gozes, O., Frid, M., Greenspan, H., and Patrick, D. (2020). Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis Article. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"027503","DOI":"10.1117\/1.JMI.4.2.027503","article-title":"Ziehl\u2013Neelsen sputum smear microscopy image database: A resource to facilitate automated bacilli detection for tuberculosis diagnosis","volume":"4","author":"Shah","year":"2017","journal-title":"J. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, Y.P., Filho, C.F.F.C., Aguilera, L.M.R., and Costa, M.G.F. (2017., January 18\u201320). Automatic classification of light field smear microscopy patches using Convolutional Neural Networks for identifying Mycobacterium Tuberculosis. Proceedings of the 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Pucon, Chile.","DOI":"10.1109\/CHILECON.2017.8229512"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kant, S., and Srivastava, M.M. (2018, January 18\u201321). Towards Automated Tuberculosis detection using Deep Learning. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bengaluru, India.","DOI":"10.1109\/SSCI.2018.8628800"},{"key":"ref_30","first-page":"691","article-title":"Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods","volume":"38","author":"Oomman","year":"2018","journal-title":"Integr. Med. Res."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mithra, K.S., and Emmanuel, W.R.S. (2019). Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis. Signal Image Video Process.","DOI":"10.1007\/s11760-019-01509-1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1007\/s00521-018-3564-4","article-title":"Tuberculosis ( TB ) detection system using deep neural networks","volume":"31","author":"Samuel","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological Image Analysis: A Review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G.V., Krpalkova, L., Riordan, D., and Walsh, J. (2020). Deep Learning vs. Traditional Computer Vision. Adv. Intell. Syst. Comput., 128\u2013144.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vajda, S., Karargyris, A., Jaeger, S., Santosh, K.C., Candemir, S., Xue, Z., Antani, S., and Thoma, G. (2018). Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs. J. Med Syst., 42.","DOI":"10.1007\/s10916-018-0991-9"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","article-title":"Automatic tuberculosis screening using chest radiographs","volume":"33","author":"Jaeger","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","first-page":"15196","article-title":"Lung tuberculosis detection using x-ray images","volume":"12","author":"Antony","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chauhan, A., Chauhan, D., and Rout, C. (2014). Role of gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0112980"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1007\/s12539-018-0313-4","article-title":"CNN\u2014MGP: Convolutional Neural Networks for Metagenomics Gene Prediction","volume":"11","author":"Allali","year":"2019","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A Few Useful Things to Know About Machine Learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the 2018 International Interdisciplinary PhD Workshop, Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shorten, C., and Khoshgoftaar, T.M. (2019). A survey on Image Data Augmentation for Deep Learning. J. Big Data, 6.","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref_44","unstructured":"O\u2019Shea, K., and Nash, R. (2015). An Introduction to Convolutional Neural Networks. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep Learning Applications in Medical Image Analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lanbouri, Z., and Achchab, S. (2015, January 20\u201321). A hybrid Deep belief network approach for Financial distress prediction. Proceedings of the 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA), Rabat, Morocco.","DOI":"10.1109\/SITA.2015.7358416"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Cao, X., Wipf, D., Wen, F., Duan, G., and Sun, J. (2013, January 1\u20138). A practical transfer learning algorithm for face verification. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.398"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","article-title":"Pulmonary Image Classification Based on Inception-v3 Transfer Learning Model","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"Krizhevsky, A., Sutskeve, I., and Hinton, G.E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_54","first-page":"1","article-title":"Comparison of Bagging and Voting Ensemble Machine Learning Algorithm as a Classifier","volume":"9","author":"Kabari","year":"2019","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chouhan, V., Singh, S.K., Khamparia, A., Gupta, D., and Albuquerque, V.H.C.D. (2020). A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci., 10.","DOI":"10.3390\/app10020559"},{"key":"ref_56","first-page":"650","article-title":"Synergy of Clustering Multiple Back Propagation Networks","volume":"2","author":"Lincoln","year":"1990","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Divina, F., Gilson, A., Gom\u00e9z-Vela, F., Torres, M.G., and Torres, J.F. (2018). Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting. Energies, 11.","DOI":"10.3390\/en11040949"},{"key":"ref_59","unstructured":"World Health Organisation (2018). Global Health TB Report, World Health Organisation."},{"key":"ref_60","first-page":"1","article-title":"Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system","volume":"10","author":"Murphy","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep25265","article-title":"An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information","volume":"6","author":"Melendez","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Heo, S.J., Kim, Y., Yun, S., Lim, S.S., Kim, J., Nam, C.M., Park, E.C., Jung, I., and Yoon, J.H. (2019). Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers\u2019 Health Examination Data. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16020250"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41598-019-42557-4","article-title":"Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization","volume":"9","author":"Pasa","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cao, Y., Liu, C., Liu, B., Brunette, M.J., Zhang, N., Sun, T., Zhang, P., Peinado, J., Garavito, E.S., and Garcia, L.L. (2016, January 27\u201329). Improving Tuberculosis Diagnostics Using Deep Learning and Mobile Health Technologies among Resource-Poor and Marginalized Communities. Proceedings of the 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE, Washington, DC, USA.","DOI":"10.1109\/CHASE.2016.18"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, Y., Wang, C., Li, A., and Meng, B. (2018). An Original Neural Network for Pulmonary Tuberculosis Diagnosis in Radiographs. Lecture Notes in Computer Science, Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece, 4\u20137 October 2018, Springer.","DOI":"10.1007\/978-3-030-01421-6_16"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Stirenko, S., Kochura, Y., and Alienin, O. (2018, January 24\u201326). Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation. Proceedings of the 2018 IEEE 38th International Conference on Electronics andNanotechnology (ELNANO), Kiev, Ukraine.","DOI":"10.1109\/ELNANO.2018.8477564"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Andika, L.A., Pratiwi, H., and Sulistijowati Handajani, S. (2020). Convolutional neural network modeling for classification of pulmonary tuberculosis disease. J. Phys. Conf. Ser., 1490.","DOI":"10.1088\/1742-6596\/1490\/1\/012020"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"22812","DOI":"10.1109\/ACCESS.2020.2970023","article-title":"Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks","volume":"8","author":"Ghafoor","year":"2020","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1093\/cid\/ciy967","article-title":"Development and Validation of a Deep Learning\u2014based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs","volume":"69","author":"Hwang","year":"2019","journal-title":"Clin. Infect. Dis."},{"key":"ref_70","first-page":"1","article-title":"A Novel Approach for Tuberculosis Screening Based on Deep Convolutional Neural Networks","volume":"9785","author":"Hwang","year":"2016","journal-title":"Med. Imaging"},{"key":"ref_71","unstructured":"Islam, M.T., Aowal, M.A., Minhaz, A.T., and Ashraf, K. (2017). Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Nguyen, Q.H., Nguyen, B.P., Dao, S.D., Unnikrishnan, B., Dhingra, R., Ravichandran, S.R., Satpathy, S., Raja, P.N., and Chua, M.C.H. (2019, January 8\u201310). Deep Learning Models for Tuberculosis Detection from Chest X-ray Images. Proceedings of the 2019 26th International Conference on Telecommunications (ICT), Hanoi, Vietnam.","DOI":"10.1109\/ICT.2019.8798798"},{"key":"ref_73","first-page":"395","article-title":"Utilizing Pretrained Deep Learning Models for Automated Pulmonary Tuberculosis Detection Using Chest Radiography","volume":"4","author":"Kieu","year":"2019","journal-title":"Intell. Inf. Database Syst."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Abbas, A., and Abdelsamea, M.M. (2018, January 18\u201319). Learning Transformations for Automated Classification of Manifestation of Tuberculosis using Convolutional Neural Network. Proceedings of the 2018 13th International Conference on Computer Engineering andSystems (ICCES), Cairo, Egypt.","DOI":"10.1109\/ICCES.2018.8639200"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Karnkawinpong, T., and Limpiyakorn, Y. (2018). Classification of pulmonary tuberculosis lesion with convolutional neural networks. J. Phys. Conf. Ser., 1195.","DOI":"10.1088\/1742-6596\/1195\/1\/012007"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, C., Cao, Y., Alcantara, M., Liu, B., Brunette, M., Peinado, J., and Curioso, W. (2017, January 17\u201320). TX-CNN: Detecting Tuberculosis in Chest X-Ray Images Using Convolutional Neural Network. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296695"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yadav, O., Passi, K., and Jain, C.K. (2018, January 3\u20136). Using Deep Learning to Classify X-ray Images of Potential Tuberculosis Patients. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine(BIBM), Madrid, Spain.","DOI":"10.1109\/BIBM.2018.8621525"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Sahlol, A.T., Elaziz, M.A., Jamal, A.T., Dama\u0161evi\u010dius, R., and Hassan, O.F. (2020). A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features. Symmetry, 12.","DOI":"10.3390\/sym12071146"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"31515","DOI":"10.1007\/s11042-019-07984-5","article-title":"Automated TB classification using ensemble of deep architectures","volume":"78","author":"Hooda","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Rashid, R., Khawaja, S.G., Akram, M.U., and Khan, A.M. (2018, January 20\u201322). Hybrid RID Network for Efficient Diagnosis of Tuberculosis from Chest X-rays. Proceedings of the 2018 9th Cairo International Biomedical Engineering Conference(CIBEC), Cairo, Egypt.","DOI":"10.1109\/CIBEC.2018.8641816"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kieu, S.T.H., Hijazi, M.H.A., Bade, A., and Saffree Jeffree, M. (2020). Tuberculosis detection using deep learning and contrast-enhanced canny edge detected x-ray images. IAES Int. J. Artif. Intell., 9.","DOI":"10.11591\/ijai.v9.i4.pp713-720"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"27318","DOI":"10.1109\/ACCESS.2020.2971257","article-title":"Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs","volume":"8","author":"Rajaraman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TMI.2014.2350539","article-title":"A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays","volume":"34","author":"Melendez","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"328","DOI":"10.5588\/ijtld.17.0520","article-title":"Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: Feasibility study","volume":"22","author":"Becker","year":"2018","journal-title":"Int. J. Tuberc. Lung Dis."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Li, L., Huang, H., and Jin, X. (2018, January 19\u201321). AE-CNN Classification of Pulmonary Tuberculosis Based on CT images. Proceedings of the 2018 9th International Conference on Information Technology inMedicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00020"},{"key":"ref_86","unstructured":"Pattnaik, A., Kanodia, S., Chowdhury, R., and Mohanty, S. (2019). Predicting Tuberculosis Related Lung Deformities from CT Scan Images Using 3D CNN, CEUR-WS."},{"key":"ref_87","unstructured":"Zunair, H., Rahman, A., and Mohammed, N. (2019). Estimating Severity from CT Scans of Tuberculosis Patients using 3D Convolutional Nets and Slice Selection, CEUR-WS."},{"key":"ref_88","unstructured":"Llopis, F., Fuster-Guillo, A., Azorin-Lopez, J., and Llopis, I. (2019). Using improved optical flow model to detect Tuberculosis, CEUR-WS."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Wardlaw, T., Johansson, E.W., and Hodge, M. (2006). Pneumonia: The Forgotten Killer of Children, United Nations Children\u2019s Fund (UNICEF).","DOI":"10.1016\/S0140-6736(06)69334-3"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Wunderink, R.G., and Waterer, G. (2017). Advances in the causes and management of community acquired pneumonia in adults. BMJ, 1\u201313.","DOI":"10.1136\/bmj.j2471"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Tobias, R.R., De Jesus, L.C.M., Mital, M.E.G., Lauguico, S.C., Guillermo, M.A., Sybingco, E., Bandala, A.A., and Dadios, E.P. (2020, January 14\u201315). CNN-based Deep Learning Model for Chest X-ray Health Classification Using TensorFlow. Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies, RIVF 2020, Ho Chi Minh, Vietnam.","DOI":"10.1109\/RIVF48685.2020.9140733"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Stephen, O., Sain, M., Maduh, U.J., and Jeong, D.U. (2019). An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare. J. Healthc. Eng., 2019.","DOI":"10.1155\/2019\/4180949"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"2649","DOI":"10.30534\/ijatcse\/2020\/24932020","article-title":"Applicability of Various Pre-Trained Deep Convolutional Neural Networks for Pneumonia Classification based on X-Ray Images","volume":"9","author":"Young","year":"2020","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"167","DOI":"10.25046\/aj050522","article-title":"Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images","volume":"5","author":"Moujahid","year":"2020","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"ref_96","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Ball, R.L., and Langlotz, C. (2017). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Rahman, T., Chowdhury, M.E.H., Khandakar, A., Islam, K.R., Islam, K.F., Mahbub, Z.B., Kadir, M.A., and Kashem, S. (2020). Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray. Appl. Sci., 10.","DOI":"10.3390\/app10093233"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Hashmi, M., Katiyar, S., Keskar, A., Bokde, N., and Geem, Z. (2020). Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning. Diagnostics, 10.","DOI":"10.3390\/diagnostics10060417"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"449","DOI":"10.13005\/bpj\/1905","article-title":"A Deep Learning Based Approach towards the Automatic Diagnosis of Pneumonia from Chest Radio-Graphs","volume":"13","author":"Acharya","year":"2020","journal-title":"Biomed. Pharmacol. J."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Elshennawy, N.M., and Ibrahim, D.M. (2020). Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images. Diagnostics, 10.","DOI":"10.3390\/diagnostics10090649"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Emhamed, R., Mamlook, A., and Chen, S. (August, January 31). Investigation of the performance of Machine Learning Classifiers for Pneumonia Detection in Chest X-ray Images. Proceedings of the 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA.","DOI":"10.1109\/EIT48999.2020.9208232"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.measurement.2019.05.076","article-title":"Identifying pneumonia in chest X-rays: A deep learning approach","volume":"145","author":"Kumar","year":"2019","journal-title":"Measurement"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1097\/RTI.0000000000000505","article-title":"Augmenting Interpretation of Chest Radiographs with Deep Learning Probability Maps","volume":"35","author":"Hurt","year":"2020","journal-title":"J. Thorac. Imaging"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.5858\/2008-132-1133-BTATCO","article-title":"Benign tumors and tumorlike conditions of the lung","volume":"132","author":"Borczuk","year":"2008","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_105","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_106","doi-asserted-by":"crossref","unstructured":"Kurniawan, E., Prajitno, P., and Soejoko, D.S. (2020). Computer-Aided Detection of Mediastinal Lymph Nodes using Simple Architectural Convolutional Neural Network. J. Phys. Conf. Ser., 1505.","DOI":"10.1088\/1742-6596\/1505\/1\/012018"},{"key":"ref_107","first-page":"1","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_108","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.measurement.2019.05.027","article-title":"Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks","volume":"145","author":"Shakeel","year":"2019","journal-title":"Meas. J. Int. Meas. Confed."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"101881","DOI":"10.1016\/j.artmed.2020.101881","article-title":"Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection","volume":"107","author":"Chen","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pmed.1002711","article-title":"Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study","volume":"15","author":"Hosny","year":"2018","journal-title":"PLoS Med."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","article-title":"Deep learning predicts lung cancer treatment response from serial medical imaging","volume":"25","author":"Xu","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_112","unstructured":"Kuan, K., Ravaut, M., Manek, G., Chen, H., Lin, J., Nazir, B., Chen, C., Howe, T.C., Zeng, Z., and Chandrasekhar, V. (2017). Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge. arXiv."},{"key":"ref_113","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 2018 11th Biomedical Engineering International Conference (BMEiCON), Chiang Mai, Thailand.","DOI":"10.1109\/BMEiCON.2018.8609997"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Xu, S., Guo, J., Zhang, G., and Bie, R. (2020). Automated detection of multiple lesions on chest X-ray images: Classification using a neural network technique with association-specific contexts. Appl. Sci., 10.","DOI":"10.3390\/app10051742"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","article-title":"Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China","volume":"395","author":"Huang","year":"2020","journal-title":"Lancet"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1111\/tmi.13383","article-title":"The COVID-19 epidemic","volume":"25","author":"Velavan","year":"2020","journal-title":"Trop. Med. Int. Health"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"100405","DOI":"10.1016\/j.imu.2020.100405","article-title":"COVID faster R\u2013CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images","volume":"20","author":"Shibly","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"620","DOI":"10.3844\/jcssp.2020.620.625","article-title":"GoogleNet CNN neural network towards chest CT-coronavirus medical image classification","volume":"16","author":"Alsharman","year":"2020","journal-title":"J. Comput. Sci."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Zhu, J., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Li, H., and Duong, T.Q. (2020). Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0236621"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Sethi, R., Mehrotra, M., and Sethi, D. (2020, January 15\u201317). Deep Learning based Diagnosis Recommendation for COVID-19 using Chest X-Rays Images. Proceedings of the 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.","DOI":"10.1109\/ICIRCA48905.2020.9183278"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s13246-020-00888-x","article-title":"Truncated inception net: COVID-19 outbreak screening using chest X-rays","volume":"43","author":"Das","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","article-title":"Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet","volume":"138","author":"Panwar","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., and Pamuk, Z. (2020). Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. arXiv.","DOI":"10.1007\/s10044-021-00984-y"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Apostolopoulos, I.D., and Mpesiana, T.A. (2020). Covid\u201419: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med., 1\u20136.","DOI":"10.1007\/s13246-020-00865-4"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E.H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Reaz, M.B.I., Mahbub, Z.B., Islam, K.R., Salman, M., and Iqbal, A. (2020). Can AI help in screening Viral and COVID-19 pneumonia?. arXiv.","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"ref_126","first-page":"1","article-title":"COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images","volume":"10","author":"Wang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_127","first-page":"643","article-title":"Detection of coronavirus disease (COVID-19) based on deep features and support vector machine","volume":"5","author":"Sethy","year":"2020","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"100412","DOI":"10.1016\/j.imu.2020.100412","article-title":"A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images","volume":"20","author":"Islam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_129","first-page":"1359","article-title":"On the detection of covid-19 from chest x-ray images using cnn-based transfer learning","volume":"64","author":"Shorfuzzaman","year":"2020","journal-title":"Comput. Mater. Contin."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1148\/radiol.2020200905","article-title":"Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy","volume":"296","author":"Li","year":"2020","journal-title":"Radiology"},{"key":"ref_131","first-page":"168","article-title":"COVID-19 prediction and detection using deep learning","volume":"12","author":"Alazab","year":"2020","journal-title":"Int. J. Comput. Inf. Syst. Ind. Manag. Appl."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","article-title":"CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection","volume":"8","author":"Waheed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/TMI.2020.2995508","article-title":"Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia","volume":"39","author":"Ouyang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"103869","DOI":"10.1016\/j.compbiomed.2020.103869","article-title":"CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization","volume":"122","author":"Mahmud","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., and Jiang, H. (2020). Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification. arXiv.","DOI":"10.1088\/1361-6560\/abe838"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Ly, S., Yu, L., Chen, Y., Su, J., and Lang, G. (2020). A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia. Engineering.","DOI":"10.1016\/j.eng.2020.04.010"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","article-title":"Classification of COVID-19 patients from chest CT images using multi-objective differential evolution\u2013based convolutional neural networks","volume":"39","author":"Singh","year":"2020","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Sedik, A., Iliyasu, A.M., El-Rahiem, B.A., Abdel Samea, M.E., Abdel-Raheem, A., Hammad, M., Peng, J., Abd El-Samie, F.E., and Abd El-Latif, A.A. (2020). Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses, 12.","DOI":"10.3390\/v12070769"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Alam, T.E., Trafalis, T., and Huebner, P. (2020). Deep MLP-CNN model using mixed-data to distinguish between COVID-19 and Non-COVID-19 patients. Symmetry, 12.","DOI":"10.3390\/sym12091526"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","article-title":"COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images","volume":"138","author":"Afshar","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.1128\/JCM.01013-17","article-title":"The TB Portals: An Open-Access, Web- Based Platform for Global Drug-Resistant- Tuberculosis Data Sharing and Analysis","volume":"55","author":"Rosenthal","year":"2017","journal-title":"J. Clin. Microbiol."},{"key":"ref_143","unstructured":"Cid, Y.D., Liauchuk, V., Klimuk, D., and Tarasau, A. (2019). Overview of ImageCLEFtuberculosis 2019\u2014Automatic CT\u2014Based Report Generation and Tuberculosis Severity Assessment, CEUR-WS."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1093\/jamia\/ocv080","article-title":"Preparing a collection of radiology examinations for distribution and retrieval","volume":"23","author":"Kohli","year":"2016","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_145","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_146","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_147","unstructured":"Xiaosong, W., Yifan, P., Le, L., Lu, Z., Mohammadhadi, B., 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 IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Costa, M.G., Filho, C.F., Kimura, A., Levy, P.C., Xavier, C.M., and Fujimoto, L.B. (2014, January 26\u201330). A sputum smear microscopy image database for automatic bacilli detection in conventional microscopy. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology ociety, EMBC, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944215"},{"key":"ref_149","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_150","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1056\/NEJMp1607591","article-title":"Toward a Shared Vision for Cancer Genomic Data","volume":"375","author":"Grossman","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., and Ghassemi, M. (2020). COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv.","DOI":"10.59275\/j.melba.2020-48g7"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/12\/131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:40:14Z","timestamp":1760179214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/6\/12\/131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,1]]},"references-count":151,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["jimaging6120131"],"URL":"https:\/\/doi.org\/10.3390\/jimaging6120131","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,1]]}}}