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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.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
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectClassification of chest diseasesspa
dc.subjectDeep learningspa
dc.titleClassification of chest diseases using deep learningspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
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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
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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
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Attribution-NonCommercial-NoDerivatives 4.0 International
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