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dc.contributor.authorSilva, Jesússpa
dc.contributor.authorZilberman, Jackspa
dc.contributor.authorPinillos-Patiño, Yiselspa
dc.contributor.authorVarela Izquierdo, Noelspa
dc.contributor.authorPineda, Omarspa
dc.date.accessioned2020-11-12T17:36:19Z
dc.date.available2020-11-12T17:36:19Z
dc.date.issued2020
dc.identifier.issn2194-5357spa
dc.identifier.urihttps://hdl.handle.net/11323/7278spa
dc.description.abstractThe field of computer vision has had exponential progress in a wide range of applications due to the use of deep learning and especially the existence of large annotated image data sets [1]. Significant improvements have been shown in the performance of problems previously considered difficult, such as object recognition, detection and segmentation over approaches based on obtaining the characteristics of the image by hand [2]. This article presents a novel method for the classification of chest diseases in the standard and widely used data set ChestX-ray8, which contains more than 100,000 front view images with 8 diseases.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectChestX-ray8spa
dc.subjectClassification of chest diseasesspa
dc.subjectDeep learningspa
dc.titleClassification of chest diseases using deep learningspa
dc.typePre-Publicaciónspa
dc.source.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089718610&doi=10.1007%2f978-3-030-53036-5_16&partnerID=40&md5=4f88abf4eec4df89f9e24a649951c350spa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-06-19
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.referencesSong, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. (2017)spa
dc.relation.referencesPouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Presa Reyes, M., Shyu, M.-L., Chen, S.-C., Iyengar, S.S.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5), 36 (2018). Article 92spa
dc.relation.referencesLitjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)spa
dc.relation.referencesWang, H., Jia, H., Lu, L., Xia, Y.: Thorax-Net: an attention regularized deep neural network for classification of Thoracic diseases on chest radiography. IEEE J. Biomed. Health Inform. 24(2), 475–485 (2019)spa
dc.relation.referencesShadeed, G.A., Tawfeeq, M.A., Mahmoud, S.M.: Deep learning model for thorax diseases detection. Telkomnika 18(1), 441–449 (2020)spa
dc.relation.referencesViloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., López, L.A.B.: Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: International Conference on Data Mining and Big Data, pp. 304–313. Springer, Cham, June 2018spa
dc.relation.referencesMao, K.P., Xie, S.P., Shao, W.Z.: Automatic Segmentation of Thorax CT Images with Fully Convolutional Networks. In: Current Trends in Computer Science and Mechanical Automation vol. 1, pp. 402–412. Sciendo Migration (2017)spa
dc.relation.referencesKrishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D. A., Bernstein, M., Fei-Fei, L.: Visual genome: Connecting language and vision using crowdsourced dense image annotations (2016)spa
dc.relation.referencesWang, X., Peng, Y., Lu, L., Lu, Z., Summers, R. M.: Automatic classification and reporting of multiple common thorax diseases using chest radiographs. In: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics, pp. 393–412. Springer, Cham (2019)spa
dc.relation.referencesTrullo, R., Petitjean, C., Ruan, S., Dubray, B., Nie, D., Shen, D.: Segmentation of organs at risk in thoracic CT images using a sharpmask architecture and conditional random fields. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 1003–1006. IEEE, April 2017spa
dc.relation.referencesWang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital- scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. arXiv preprint arXiv:1705.02315 (2017)spa
dc.relation.referencesMing, J.T.C., Noor, N.M., Rijal, O.M., Kassim, R.M., Yunus, A.: Lung disease classification using different deep learning architectures and principal component analysis. In: 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), pp. 187–190. IEEE, July 2018spa
dc.relation.referencesYao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., Lyman, K.: Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501 (2017)spa
dc.relation.referencesDodge, S., Karam, L.: Understanding how image quality affects deep neural networks. In: Eighth International Conference on Quality of Multimedia Experience (QoMEX), IEEE, pp. 1–6. arXiv:1604.04004v2 (2016)spa
dc.relation.referencesWang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)spa
dc.relation.referencesLiu, Z., Chen, H., Liu, H.: Deep Learning Based Framework for Direct Reconstruction of PET Images. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 48–56. Springer, Cham, October 2019spa
dc.relation.referencesGamero, W.M., Agudelo-Castañeda, D., Ramirez, M. C., Hernandez, M. M., Mendoza, H. P., Parody, A., Viloria, A.: Hospital admission and risk assessment associated to exposure of fungal bioaerosols at a municipal landfill using statistical models. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 210–218. Springer, Cham, November 2018spa
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dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/preprintspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTOTRspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_14cbspa


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Attribution-NonCommercial-NoDerivatives 4.0 International
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