Classification of chest diseases using deep learning

dc.creatorSilva, Jesús
dc.creatorZilberman, Jack
dc.creatorPinillos-Patiño, Yisel
dc.creatorVarela Izquierdo, Noel
dc.creatorPineda, Omar
dc.date2020-11-12T17:36:19Z
dc.date2020-11-12T17:36:19Z
dc.date2020
dc.date2021-06-19
dc.date.accessioned2023-10-03T20:11:28Z
dc.date.available2023-10-03T20:11:28Z
dc.identifier2194-5357
dc.identifierhttps://hdl.handle.net/11323/7278
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174682
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relationSong, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. (2017)
dc.relationPouyanfar, 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 92
dc.relationLitjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
dc.relationWang, 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)
dc.relationShadeed, G.A., Tawfeeq, M.A., Mahmoud, S.M.: Deep learning model for thorax diseases detection. Telkomnika 18(1), 441–449 (2020)
dc.relationViloria, 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 2018
dc.relationMao, 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)
dc.relationKrishna, 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)
dc.relationWang, 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)
dc.relationTrullo, 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 2017
dc.relationWang, 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)
dc.relationMing, 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 2018
dc.relationYao, 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)
dc.relationDodge, 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)
dc.relationWang, 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)
dc.relationLiu, 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 2019
dc.relationGamero, 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 2018
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.rightshttp://purl.org/coar/access_right/c_14cb
dc.sourceAdvances in Intelligent Systems and Computing
dc.sourcehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089718610&doi=10.1007%2f978-3-030-53036-5_16&partnerID=40&md5=4f88abf4eec4df89f9e24a649951c350
dc.subjectChestX-ray8
dc.subjectClassification of chest diseases
dc.subjectDeep learning
dc.titleClassification of chest diseases using deep learning
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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