Classification of chest diseases using deep learning
The 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...
- Autores:
-
Silva, Jesús
Zilberman, Jack
Pinillos-Patiño, Yisel
Varela Izquierdo, Noel
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7278
- Acceso en línea:
- https://hdl.handle.net/11323/7278
https://repositorio.cuc.edu.co/
- Palabra clave:
- ChestX-ray8
Classification of chest diseases
Deep learning
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Classification of chest diseases using deep learning |
| title |
Classification of chest diseases using deep learning |
| spellingShingle |
Classification of chest diseases using deep learning ChestX-ray8 Classification of chest diseases Deep learning |
| title_short |
Classification of chest diseases using deep learning |
| title_full |
Classification of chest diseases using deep learning |
| title_fullStr |
Classification of chest diseases using deep learning |
| title_full_unstemmed |
Classification of chest diseases using deep learning |
| title_sort |
Classification of chest diseases using deep learning |
| dc.creator.fl_str_mv |
Silva, Jesús Zilberman, Jack Pinillos-Patiño, Yisel Varela Izquierdo, Noel Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Zilberman, Jack Pinillos-Patiño, Yisel Varela Izquierdo, Noel Pineda, Omar |
| dc.subject.spa.fl_str_mv |
ChestX-ray8 Classification of chest diseases Deep learning |
| topic |
ChestX-ray8 Classification of chest diseases Deep learning |
| description |
The 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. |
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2020 |
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2020-11-12T17:36:19Z |
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2020 |
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2021-06-19 |
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http://purl.org/coar/resource_type/c_816b |
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Corporación Universidad de la Costa |
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eng |
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Song, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. (2017) Pouyanfar, 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 Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017) Wang, 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) Shadeed, G.A., Tawfeeq, M.A., Mahmoud, S.M.: Deep learning model for thorax diseases detection. Telkomnika 18(1), 441–449 (2020) Viloria, 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 Mao, 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) Krishna, 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) Wang, 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) Trullo, 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 Wang, 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) Ming, 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 Yao, 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) Dodge, 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) Wang, 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) Liu, 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 Gamero, 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 |
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Silva, JesúsZilberman, JackPinillos-Patiño, YiselVarela Izquierdo, NoelPineda, Omar2020-11-12T17:36:19Z2020-11-12T17:36:19Z20202021-06-192194-5357https://hdl.handle.net/11323/7278Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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.Silva, JesúsZilberman, Jack-will be generated-orcid-0000-0003-0956-4059-600Pinillos-Patiño, Yisel-will be generated-orcid-0000-0001-5047-3883-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089718610&doi=10.1007%2f978-3-030-53036-5_16&partnerID=40&md5=4f88abf4eec4df89f9e24a649951c350ChestX-ray8Classification of chest diseasesDeep learningClassification of chest diseases using deep learningPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionSong, Q., Zhao, L., Luo, X., Dou, X.: Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. (2017)Pouyanfar, 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 92Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)Wang, 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)Shadeed, G.A., Tawfeeq, M.A., Mahmoud, S.M.: Deep learning model for thorax diseases detection. Telkomnika 18(1), 441–449 (2020)Viloria, 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 2018Mao, 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)Krishna, 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)Wang, 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)Trullo, 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 2017Wang, 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)Ming, 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 2018Yao, 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)Dodge, 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)Wang, 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)Liu, 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 2019Gamero, 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 2018PublicationORIGINALCLASSIFICATION OF CHEST DISEASES USING DEEP LEARNING.pdfCLASSIFICATION OF CHEST DISEASES USING DEEP LEARNING.pdfapplication/pdf6012https://repositorio.cuc.edu.co/bitstreams/622032a7-23bc-40c3-9e43-e1867d253230/downloadd36f4edba5d65dffa0f48b846d6dd68fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/57bd516f-9d49-41b8-8f04-fcf0a84007be/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/30e41645-0aca-477f-8ba4-feb890aa73ab/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILCLASSIFICATION OF CHEST DISEASES USING DEEP LEARNING.pdf.jpgCLASSIFICATION OF CHEST DISEASES USING DEEP LEARNING.pdf.jpgimage/jpeg36269https://repositorio.cuc.edu.co/bitstreams/9a6b2aa4-b536-4c09-b629-51e8ebc813f3/downloadef5ef1f131b7feda29d85e29ca0c5365MD54TEXTCLASSIFICATION OF CHEST DISEASES USING DEEP 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