{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:08:36Z","timestamp":1743016116801,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030657741"},{"type":"electronic","value":"9783030657758"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-65775-8_6","type":"book-chapter","created":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T15:02:57Z","timestamp":1608390177000},"page":"57-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["COVID-19 X-ray Image Diagnostic with Deep Neural Networks"],"prefix":"10.1007","author":[{"given":"Gabriel","family":"Oliveira","sequence":"first","affiliation":[]},{"given":"Rafael","family":"Padilha","sequence":"additional","affiliation":[]},{"given":"Andr\u00e9","family":"Dorte","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Cereda","sequence":"additional","affiliation":[]},{"given":"Luiz","family":"Miyazaki","sequence":"additional","affiliation":[]},{"given":"Maur\u00edcio","family":"Lopes","sequence":"additional","affiliation":[]},{"given":"Zanoni","family":"Dias","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,20]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1007\/s13246-020-00865-4","volume":"43","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos, I.D., Mpesiana, T.A.: Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43(2), 635\u2013640 (2020). https:\/\/doi.org\/10.1007\/s13246-020-00865-4","journal-title":"Phys. Eng. Sci. Med."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1016\/j.csbj.2020.03.025","volume":"18","author":"BR Beck","year":"2020","unstructured":"Beck, B.R., Shin, B., Choi, Y., Park, S., Kang, K.: Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784\u2013790 (2020)","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1109\/RBME.2018.2885714","volume":"12","author":"P Bizopoulos","year":"2018","unstructured":"Bizopoulos, P., Koutsouris, D.: Deep learning in cardiology. IEEE Rev. Biomed. Eng. 12, 168\u2013193 (2018)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: ACM International Conference on Knowledge Discovery and Data Mining (ACM KDD), pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"6499","key":"6_CR7","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1126\/science.abc1932","volume":"369","author":"Q Gao","year":"2020","unstructured":"Gao, Q., Bao, L., Mao, H., Wang, L., Xu, K., Yang, M., Li, Y., Zhu, L., Wang, N., Lv, Z., et al.: Development of an inactivated vaccine candidate for SARS-CoV-2. Science 369(6499), 77\u201381 (2020)","journal-title":"Science"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR9","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 (2017)"},{"issue":"10223","key":"6_CR10","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al.: Clinical features of patients infected with 2019 novel coronavirus in wuhan, china. The Lancet 395(10223), 497\u2013506 (2020)","journal-title":"The Lancet"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Kawahara, J., Hamarneh, G.: Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: International Workshop on Machine Learning in Medical Imaging, pp. 164\u2013171 (2016)","DOI":"10.1007\/978-3-319-47157-0_20"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"110059","DOI":"10.1016\/j.chaos.2020.110059","volume":"139","author":"S Lalmuanawma","year":"2020","unstructured":"Lalmuanawma, S., Hussain, J., Chhakchhuak, L.: Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals 139, 110059 (2020)","journal-title":"Chaos, Solitons & Fractals"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv:2003.10849 (2020)","DOI":"10.1007\/s10044-021-00984-y"},{"key":"6_CR16","unstructured":"Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: understanding transfer learning for medical imaging. In: Advances in Neural Information Processing Systems (NIPS), pp. 3347\u20133357 (2019)"},{"key":"6_CR17","unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv:1711.05225 (2017)"},{"issue":"4","key":"6_CR18","doi-asserted-by":"publisher","first-page":"e0232391","DOI":"10.1371\/journal.pone.0232391","volume":"15","author":"GS Randhawa","year":"2020","unstructured":"Randhawa, G.S., Soltysiak, M.P., El Roz, H., de Souza, C.P., Hill, K.A., Kari, L.: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: Covid-19 case study. PLoS ONE 15(4), e0232391 (2020)","journal-title":"PLoS ONE"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Shen, W., Zhou, M., Yang, F., Yang, C., Tian, J.: Multi-scale convolutional neural networks for lung nodule classification. In: International Conference on Information Processing in Medical Imaging (IPMI), pp. 588\u2013599 (2015)","DOI":"10.1007\/978-3-319-19992-4_46"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"6_CR22","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: IEEE International Conference on Machine Learning (ICML), pp. 6105\u20136114 (2019)"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"100222","DOI":"10.1016\/j.iot.2020.100222","volume":"11","author":"S Tuli","year":"2020","unstructured":"Tuli, S., Tuli, S., Tuli, R., Gill, S.S.: Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things 11, 100222 (2020)","journal-title":"Internet of Things"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Wang, L., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. arXiv:2003.09871 (2020)","DOI":"10.1038\/s41598-020-76550-z"},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42256-020-0144-y","volume":"2","author":"L Yan","year":"2020","unstructured":"Yan, L., et al.: An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2, 1\u20136 (2020)","journal-title":"Nat. Mach. Intell."},{"key":"6_CR26","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems (NIPS), pp. 3320\u20133328 (2014)"},{"issue":"8","key":"6_CR27","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1007\/s11517-016-1590-x","volume":"55","author":"J Zhao","year":"2016","unstructured":"Zhao, J., Zhang, M., Zhou, Z., Chu, J., Cao, F.: Automatic detection and classification of leukocytes using convolutional neural networks. Med. Biol. Eng. Comput. 55(8), 1287\u20131301 (2016). https:\/\/doi.org\/10.1007\/s11517-016-1590-x","journal-title":"Med. Biol. Eng. Comput."},{"issue":"7798","key":"6_CR28","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1038\/s41586-020-2012-7","volume":"579","author":"P Zhou","year":"2020","unstructured":"Zhou, P., Yang, X.L., Wang, X.G., Hu, B., Zhang, L., Zhang, W., Si, H.R., Zhu, Y., Li, B., Huang, C.L., et al.: A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798), 270\u2013273 (2020)","journal-title":"Nature"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8697\u20138710 (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Lecture Notes in Computer Science","Advances in Bioinformatics and Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65775-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T02:01:56Z","timestamp":1670378516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65775-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030657741","9783030657758"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65775-8_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"20 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BSB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Symposium on Bioinformatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wob2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bsb.sbc.org.br\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19 pandemic the conference was held virtually","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}