{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:02:42Z","timestamp":1742922162146,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811610851"},{"type":"electronic","value":"9789811610868"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-981-16-1086-8_34","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T22:48:05Z","timestamp":1616712485000},"page":"387-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transfer Learning Based COVID-19 Patient Classification"],"prefix":"10.1007","author":[{"given":"Vrinda","family":"Rastogi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahima","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chandra","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rishav","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"34_CR1","unstructured":"Fauci, A.S., Lane, H.C., Redfield, R.R.: Covid-19\u2013navigating the uncharted. New Engl. J. Med. 382(13), 1268\u20131269 (2020)"},{"issue":"5","key":"34_CR2","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1038\/s41569-020-0360-5","volume":"17","author":"Y-Y Zheng","year":"2020","unstructured":"Zheng, Y.-Y., Ma, Y.-T., Zhang, J.-Y., Xie, X.: Covid-19 and the cardiovascular system. Nat. Rev. Cardiol. 17(5), 259\u2013260 (2020)","journal-title":"Nat. Rev. Cardiol."},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Chakraborty, I., Maity, P.: Covid-19 outbreak: Migration, effects on society, global environment and prevention. Sci. Total Environ. 728, 138882 (2020)","DOI":"10.1016\/j.scitotenv.2020.138882"},{"key":"34_CR4","unstructured":"Self, W.H., Courtney, D.M., McNaughton, C.D., Wunderink, R.G., Kline, J.A.: High discordance of chest x-ray and computed tomography for detection of pulmonary opacities in ed patients: implications for diagnosing pneumonia. Am. J. Emerg. Med. 31(2), 401\u2013405 (2013)"},{"key":"34_CR5","unstructured":"Nair, A., et al.: A british society of thoracic imaging statement: considerations in designing local imaging diagnostic algorithms for the covid-19 pandemic. Clin. Radiol. 75(5), 329\u2013334 (2020)"},{"key":"34_CR6","doi-asserted-by":"publisher","unstructured":"Shi, F., et al.:. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 1\u20131 (2020). https:\/\/doi.org\/10.1109\/RBME.2020.2987975","DOI":"10.1109\/RBME.2020.2987975"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Breslow , N.E., Lin, X.: Bias correction in generalised linear mixed models with a single component of dispersion. Biometrika 82(1), 81\u201391 (1995)","DOI":"10.1093\/biomet\/82.1.81"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Litjens, G.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","DOI":"10.1016\/j.media.2017.07.005"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of covid-19 cases using deep neural networks with x-ray images. Comput. Biol. Med. 121, 103792 (2020)","DOI":"10.1016\/j.compbiomed.2020.103792"},{"key":"34_CR10","unstructured":"Hemdan, E.E.D., Shouman, M.A., Karar. , M.E.: Covidx-net: a framework of deep learning classifiers to diagnose covid-19 in x-ray images (2020)"},{"key":"34_CR11","unstructured":"Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of covid-19 in chest x-ray images using detrac deep convolutional neural network (2020). arXiv:2003.13815"},{"key":"34_CR12","unstructured":"Tartaglione, E., Barbano, C.A., Berzovini, C., Calandri, M., Grangetto, M.: Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data (2020). arXiv:2004.05405"},{"key":"34_CR13","unstructured":"Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Soufi, G.J.: Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning (2020). arXiv:2004.09363"},{"key":"34_CR14","unstructured":"Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: Covid-19 image data collection: Prospective predictions are the future (2020). arXiv:2006.11988"},{"key":"34_CR15","doi-asserted-by":"crossref","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), 1122\u20131131 (2018)","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J. VLSI Signal Process. Syste. Signal, Image and Video Technol. 38(1), 35\u201344 (2004)","DOI":"10.1023\/B:VLSI.0000028532.53893.82"},{"key":"34_CR17","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv:1409.1556"},{"issue":"4","key":"34_CR18","first-page":"126","volume":"13","author":"P Dhankhar","year":"2019","unstructured":"Dhankhar, P.: Resnet-50 and vgg-16 for recognizing facial emotions. Int. J. Innov. Eng. Technol. 13(4), 126\u2013130 (2019)","journal-title":"Int. J. Innov. Eng. Technol."},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., De Geus, P.: Malicious software classification using transfer learning of resnet-50 deep neural network. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1011\u20131014. IEEE (2017)","DOI":"10.1109\/ICMLA.2017.00-19"},{"key":"34_CR21","unstructured":"Wen, L., Li, X., Gao, L.: A transfer convolutional neural network for fault diagnosis based on resnet-50. Neural Comput. Appl. 31, 1\u201314 (2019)"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Reddy, A.S.B., Juliet, D.S.: Transfer learning with resnet-50 for malaria cell-image classification. In: 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0945\u20130949. IEEE (2019)","DOI":"10.1109\/ICCSP.2019.8697909"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Chollet. F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"34_CR25","doi-asserted-by":"crossref","unstructured":"Jaworek-Korjakowska, J., Kleczek, P., Gorgon, M.: Melanoma thickness prediction based on convolutional neural network with vgg-19 model transfer learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00333"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"34_CR27","unstructured":"Baldassarre, F., Mor\u00edn, D.G., Rod\u00e9s-Guirao, L.: Deep koalarization: Image colorization using cnns and inception-resnet-v2 (2017). arXiv:1712.03400"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Kumar, M., Thenmozhi, M.: Forecasting stock index movement: A comparison of support vector machines and random forest. In: Indian Institute of Capital Markets 9th Capital Markets Conference Paper (2006)","DOI":"10.2139\/ssrn.876544"},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas M.: Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol. Rev. 71, 804\u2013818 (2015)","DOI":"10.1016\/j.oregeorev.2015.01.001"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Lameski, P., Zdravevski, E., Mingov, R., Kulakov, A.: Svm parameter tuning with grid search and its impact on reduction of model over-fitting. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 464\u2013474. Springer (2015)","DOI":"10.1007\/978-3-319-25783-9_41"}],"container-title":["Communications in Computer and Information Science","Computer Vision and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-1086-8_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T13:41:11Z","timestamp":1671716471000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1086-8_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811610851","9789811610868"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1086-8_34","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CVIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Vision and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prayagraj","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cvip2020.iiita.ac.in","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"352","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":"134","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":"0","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":"38% - 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":"4","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 the COVID-19 pandemic the conference was partially held in a virtual mode.","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)"}}]}}