{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T01:11:11Z","timestamp":1778980271918,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["Brazilian scholarships"],"award-info":[{"award-number":["Brazilian scholarships"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["Brazilian scholarships"],"award-info":[{"award-number":["Brazilian scholarships"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["Newton International Fellow Alumnus"],"award-info":[{"award-number":["Newton International Fellow Alumnus"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.<\/jats:p>","DOI":"10.3390\/info11120548","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T09:04:15Z","timestamp":1606381455000},"page":"548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Convolutional Support Vector Models: Prediction of Coronavirus Disease Using Chest X-rays"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7056-386X","authenticated-orcid":false,"given":"Mateus","family":"Maia","sequence":"first","affiliation":[{"name":"Hamilton Institute, Mathematics and Statistics, Maynooth University, County Kildare, W23 F2K8 Maynooth, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonatha S.","family":"Pimentel","sequence":"additional","affiliation":[{"name":"Statistics Department, Federal University of Bahia, Salvador 40170-110, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivalbert S.","family":"Pereira","sequence":"additional","affiliation":[{"name":"Statistics Department, Federal University of Bahia, Salvador 40170-110, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o","family":"Gondim","sequence":"additional","affiliation":[{"name":"Computer Science Department, Federal University of Bahia, Salvador 40170-110, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7818-1855","authenticated-orcid":false,"given":"Marcos E.","family":"Barreto","sequence":"additional","affiliation":[{"name":"Computer Science Department, Federal University of Bahia, Salvador 40170-110, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1041-2768","authenticated-orcid":false,"given":"Anderson","family":"Ara","sequence":"additional","affiliation":[{"name":"Statistics Department, Federal University of Bahia, Salvador 40170-110, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/S0140-6736(20)30185-9","article-title":"A novel coronavirus outbreak of global health concern","volume":"395","author":"Wang","year":"2020","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.cell.2020.06.043","article-title":"Tracking changes in SARS-CoV-2 Spike: Evidence that D614G increases infectivity of the COVID-19 virus","volume":"182","author":"Korber","year":"2020","journal-title":"Cell"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1002\/jmv.25722","article-title":"Understanding of COVID-19 based on current evidence","volume":"92","author":"Sun","year":"2020","journal-title":"J. Med. Virol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e61","DOI":"10.3346\/jkms.2020.35.e61","article-title":"The first case of 2019 novel coronavirus pneumonia imported into Korea from Wuhan, China: Implication for infection prevention and control measures","volume":"35","author":"Kim","year":"2020","journal-title":"J. Korean Med. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, G., Wang, W., Moon, J., Pack, J.K., and Jeon, S.I. (2011, January 21\u201325). A review of breast tissue classification in mammograms. Proceedings of the 2011 ACM Symposium on Research in Applied Computation, Taichung, Taiwan.","DOI":"10.1145\/2103380.2103426"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"El-Yaagoubi, M., Mora-Jim\u00e9nez, I., Jabrane, Y., Mu\u00f1oz-Romero, S., Rojo-\u00c1lvarez, J.L., and Pareja-Grande, J.A. (2020). Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification. Information, 11.","DOI":"10.3390\/info11080393"},{"key":"ref_9","first-page":"519","article-title":"Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review","volume":"10","author":"Pellegrini","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cmpb.2017.12.012","article-title":"Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review","volume":"156","author":"Yassin","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.procs.2016.04.224","article-title":"Using machine learning algorithms for breast cancer risk prediction and diagnosis","volume":"83","author":"Asri","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s10462-017-9552-8","article-title":"Machine learning based decision support systems (DSS) for heart disease diagnosis: A review","volume":"50","author":"Safdar","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: A review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bakator, M., and Radosav, D. (2018). Deep learning and medical diagnosis: A review of literature. Multimodal Technol. Interact., 2.","DOI":"10.3390\/mti2030047"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tzotsos, A., and Argialas, D. (2008). Support vector machine classification for object-based image analysis. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_36"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/TSMCC.2002.807277","article-title":"Robust support vector machine with bullet hole image classification","volume":"32","author":"Song","year":"2002","journal-title":"IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.bspc.2006.05.002","article-title":"Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network","volume":"1","author":"Chaplot","year":"2006","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MGRS.2016.2641240","article-title":"Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques","volume":"5","author":"Maulik","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Islam, M., Dinh, A., Wahid, K., and Bhowmik, P. (May, January 30). Detection of potato diseases using image segmentation and multiclass support vector machine. Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"ref_22","unstructured":"Huang, F.J., and LeCun, Y. (2006, January 17\u201322). Large-scale Learning with SVM and Convolutional Nets for Generic Object Categorization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s12065-018-0167-z","article-title":"Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification","volume":"13","author":"Tashiev","year":"2020","journal-title":"Evol. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101808","DOI":"10.1016\/j.artmed.2020.101808","article-title":"Classification of glomerular hypercellularity using convolutional features and support vector machine","volume":"103","author":"Chagas","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neunet.2017.02.005","article-title":"Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation","volume":"92","author":"Witoonchart","year":"2017","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zafar, R., Malik, A.S., Shuaibu, A.N., ur Rehman, M.J., and Dass, S.C. (2017, January 12\u201314). Classification of fmri data using support vector machine and convolutional neural network. Proceedings of the 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuching, Malaysia.","DOI":"10.1109\/ICSIPA.2017.8120630"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1166\/jmihi.2020.2889","article-title":"Skin Cancer Diagnosis Based on Support Vector Machine and a New Optimization Algorithm","volume":"10","author":"Li","year":"2020","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1590\/1516-4446-2016-2083","article-title":"Support vector machine-based classification of neuroimages in Alzheimer\u2019s disease: Direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals","volume":"40","author":"Ferreira","year":"2018","journal-title":"Braz. J. Psychiatry"},{"key":"ref_29","first-page":"71","article-title":"Texture analysis of ultrasound medical images for diagnosis of thyroid nodule using support vector machine","volume":"2","author":"Kale","year":"2013","journal-title":"Int. J. Comput. Sci. Mob. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1016\/j.neubiorev.2012.01.004","article-title":"Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review","volume":"36","author":"Orru","year":"2012","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_31","unstructured":"Novitasari, D.C.R., Hendradi, R., Caraka, R.E., Rachmawati, Y., Fanani, N.Z., Syarifudin, A., Toharudin, T., and Chen, R.C. (2020). Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network. Commun. Math. Biol. Neurosci., 2020."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., and Behera, S.K. (2020). Detection of coronavirus disease (covid-19) based on deep features. Preprints, 030300.","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tayarani-N, M.H. (2020). Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review. Chaos Solitons Fractals, 110338.","DOI":"10.1016\/j.chaos.2020.110338"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, D., Liu, F., and Li, Z. (2020). A Review of Automatically Diagnosing COVID-19 based on Scanning Image. arXiv.","DOI":"10.1145\/3449301.3449778"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nishio, M., Noguchi, S., Matsuo, H., and Murakami, T. (2020). Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: Combination of data augmentation methods in a small dataset. arXiv.","DOI":"10.1038\/s41598-020-74539-2"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Luz, E.J.D.S., Silva, P.L., Silva, R., Silva, L., Moreira, G., and Menotti, D. (2020). Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images. arXiv.","DOI":"10.1007\/s42600-021-00151-6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","article-title":"Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks","volume":"43","author":"Apostolopoulos","year":"2020","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104284","DOI":"10.1016\/j.ijmedinf.2020.104284","article-title":"Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms","volume":"144","author":"Heidari","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cao, K., Choi, K.N., Jung, H., and Duan, L. (2020). Deep Learning for Facial Beauty Prediction. Information, 11.","DOI":"10.3390\/info11080391"},{"key":"ref_40","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_41","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2017). Data Mining Practical Learning Tools and Techniques, Morgan Kaufmann. [4rd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, B., Sun, Y., Xue, B., and Zhang, M. (2018, January 8\u201313). Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification. Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477735"},{"key":"ref_43","unstructured":"Ciaburro, G., and Venkateswaran, B. (2017). Neural Networks with R, Packt Publishing."},{"key":"ref_44","unstructured":"Haykin, S. (2009). Neural Networks and Learning Machines, Pearson Education, Inc.. [3rd ed.]."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Nagi, J., Di Caro, G.A., Giusti, A., Nagi, F., and Gambardella, L.M. (2012, January 12\u201315). Convolutional Neural Support Vector Machines: Hybrid visual pattern classifiers for multirobot systems. Proceedings of the 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.14"},{"key":"ref_46","unstructured":"Tang, Y. (2015). Deep Learning using Linear Support Vector Machines. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","author":"Vapnik","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/S0925-2312(02)00632-X","article-title":"Determination of the spread parameter in the Gaussian kernel for classification and regression","volume":"55","author":"Wang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_49","unstructured":"Yaohao, P. (2016). Support Vector Regression Aplicado \u00e0 Previs\u00e3o de Taxas de C\u00e2mbio. [Master\u2019s Thesis, Universidade de Brasilia]."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15202","DOI":"10.1016\/j.eswa.2011.05.081","article-title":"Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool","volume":"38","author":"Elangovan","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"281","DOI":"10.5194\/isprsarchives-XL-2-W3-281-2014","article-title":"A comparison study of different kernel functions for SVM-based classification of multi-temporal polarimetry SAR data","volume":"40","author":"Yekkehkhany","year":"2014","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106303","DOI":"10.1016\/j.agwat.2020.106303","article-title":"A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources","volume":"240","author":"Chen","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_53","unstructured":"Sarmento, P.L. (2014). Avalia\u00e7\u00e3o de m\u00e9Todos de Sele\u00e7\u00e3o de Amostras para Redu\u00e7\u00e3o do Tempo de Treinamento do Classificador SVM. [Master\u2019s Thesis, INPE]."},{"key":"ref_54","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the NIPS 2012, Lake Tahoe, NV, USA."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: An overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_56","unstructured":"Chollet, F., and Allaire, J. (2020, November 20). R Interface to Keras. Available online: https:\/\/github.com\/rstudio\/keras."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v011.i09","article-title":"kernlab\u2014An S4 Package for Kernel Methods in R","volume":"11","author":"Karatzoglou","year":"2004","journal-title":"J. Stat. Softw."},{"key":"ref_58","unstructured":"Caputo, B., Sim, K., Furesjo, F., and Smola, A. (2002, January 9\u201314). Appearance-based object recognition using SVMs: Which kernel should I use?. Proceedings of the NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision, Vancouver, BC, Canada."},{"key":"ref_59","unstructured":"Radiopedia (2020, August 15). Chest (AP Erect View). Available online: https:\/\/radiopaedia.org\/articles\/chest-ap-erect-view-1."},{"key":"ref_60","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"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSE.2016.2584050","article-title":"An empirical comparison of model validation techniques for defect prediction models","volume":"43","author":"Tantithamthavorn","year":"2016","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/0020-0190(87)90114-1","article-title":"Occam\u2019s razor","volume":"24","author":"Blumer","year":"1987","journal-title":"Inf. Process. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1023\/A:1009868929893","article-title":"The role of Occam\u2019s razor in knowledge discovery","volume":"3","author":"Domingos","year":"1999","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Osuna, E., Freund, R., and Girosi, F. (1997). An improved training algorithm for support vector machines. Neural Networks for Signal Processing VII, Proceedings of the 1997 IEEE Signal Processing Society Workshop, Amelia Island, FL, USA, USA, 24\u201326 September 1997, IEEE.","DOI":"10.1109\/NNSP.1997.622408"},{"key":"ref_65","first-page":"293","article-title":"Exact simplification of support vector solutions","volume":"2","author":"Downs","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TNNLS.2012.2186314","article-title":"Reducing the number of support vectors of SVM classifiers using the smoothed separable case approximation","volume":"23","author":"Geebelen","year":"2012","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kim, H., Nam, H., Jung, W., and Lee, J. (2017, January 24\u201325). Performance analysis of CNN frameworks for GPUs. Proceedings of the 2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), Santa Rosa, CA, USA.","DOI":"10.1109\/ISPASS.2017.7975270"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","article-title":"State-of-the-art deep learning in cardiovascular image analysis","volume":"12","author":"Litjens","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"gix083","DOI":"10.1093\/gigascience\/gix083","article-title":"Deep machine learning provides state-of-the-art performance in image-based plant phenotyping","volume":"6","author":"Pound","year":"2017","journal-title":"Gigascience"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1002\/jmri.26534","article-title":"Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI","volume":"49","author":"Mazurowski","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/12\/548\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:37:31Z","timestamp":1760179051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/12\/548"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,26]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["info11120548"],"URL":"https:\/\/doi.org\/10.3390\/info11120548","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,26]]}}}