{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:36:28Z","timestamp":1764174988508,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"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_14","type":"book-chapter","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T22:48:05Z","timestamp":1616712485000},"page":"149-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Diagnosis of COVID-19 from CT Scans Based on Concatenation of Mobilenetv2 and ResNet50 Features"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-3957","authenticated-orcid":false,"given":"Taranjit","family":"Kaur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3532-9389","authenticated-orcid":false,"given":"Tapan Kumar","family":"Gandhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"key":"14_CR1","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., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497\u2013506 (2020)","journal-title":"Lancet"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1056\/NEJMoa2001017","volume":"382","author":"N Zhu","year":"2020","unstructured":"Zhu, N., et al.: A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382, 727\u2013733 (2020)","journal-title":"N. Engl. J. Med."},{"key":"14_CR3","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","volume":"395","author":"F Zhou","year":"2020","unstructured":"Zhou, F., et al.: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, 1054\u20131062 (2020)","journal-title":"Lancet"},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"200463","DOI":"10.1148\/radiol.2020200463","volume":"295","author":"A Bernheim","year":"2020","unstructured":"Bernheim, A., et al.: Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295, 200463 (2020)","journal-title":"Radiology"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/rg.2018170048","volume":"38","author":"HJ Koo","year":"2018","unstructured":"Koo, H.J., Lim, S., Choe, J., Choi, S.-H., Sung, H., Do, K.-H.: Radiographic and CT features of viral pneumonia. Radiographics 38, 719\u2013739 (2018)","journal-title":"Radiographics"},{"key":"14_CR6","unstructured":"Gozes, O., et al.: Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv Prepr. arXiv2003.05037 (2020)"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1148\/radiol.2019181960","volume":"292","author":"J Choe","year":"2019","unstructured":"Choe, J., et al.: Deep learning\u2013based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292, 365\u2013373 (2019)","journal-title":"Radiology"},{"key":"14_CR8","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany, D.S., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122\u20131131 (2018)","journal-title":"Cell"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study. medRxiv (2020)","DOI":"10.1101\/2020.02.25.20021568"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv (2020)","DOI":"10.1101\/2020.02.14.20023028"},{"key":"14_CR11","unstructured":"Zhao, J., Zhang, Y., He, X., Xie, P.: COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv Prepr. arXiv2003.13865 (2020)"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Loey, M., Smarandache, F., Khalifa, N.E.M.: A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images (2020)","DOI":"10.20944\/preprints202004.0252.v1"},{"key":"14_CR13","unstructured":"Soares, E., Angelov, P., Biaso, S., Froes, M.H., Abe, D.K.: SARS-CoV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv (2020)"},{"key":"14_CR14","unstructured":"Howard, A.G., et al.: Efficient convolutional neural networks for mobile vision applications. arXiv Prepr. arXiv1704.04861 (2017)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"14_CR16","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":"14_CR17","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Sonawane, P.K., Shelke, S.: Handwritten devanagari character classification using deep learning. In: 2018 International Conference on Information, Communication, Engineering and Technology (ICICET), pp. 1\u20134 (2018)","DOI":"10.1109\/ICICET.2018.8533703"},{"key":"14_CR19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jocs.2018.11.008","volume":"30","author":"S Lu","year":"2019","unstructured":"Lu, S., Lu, Z., Zhang, Y.-D.: Pathological brain detection based on AlexNet and transfer learning. J. Comput. Sci. 30, 41\u201347 (2019)","journal-title":"J. Comput. Sci."},{"key":"14_CR20","unstructured":"Graf, H.P., Cosatto, E., Bottou, L., Dourdanovic, I., Vapnik, V.: Parallel support vector machines: the cascade SVM. In: Advances in Neural Information Processing Systems, pp. 521\u2013528 (2005)"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Pathak, Y., Shukla, P.K., Arya, K.V.: Deep bidirectional classification model for COVID-19 disease infected patients. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2020)","DOI":"10.1109\/TCBB.2020.3009859"},{"key":"14_CR22","doi-asserted-by":"publisher","first-page":"100427","DOI":"10.1016\/j.imu.2020.100427","volume":"20","author":"P Silva","year":"2020","unstructured":"Silva, P., et al.: COVID-19 detection in CT images with deep learning: a voting-based scheme and cross-datasets analysis. Inform. Med. Unlocked 20, 100427 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"He, X., et al.: Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. medRxiv (2020)","DOI":"10.1101\/2020.04.13.20063941"},{"key":"14_CR24","unstructured":"Hasan, M., Alam, M., Elahi, M., Toufick, E., Roy, S., Wahid, S.R., et al.: CVR-Net: a deep convolutional neural network for coronavirus recognition from chest radiography images. arXiv Prepr. arXiv2007.11993 (2020)"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Saqib, M., Anwar, S., Anwar, A., Blumenstein, M., et al.: COVID19 detection from radiographs: is deep learning able to handle the crisis? (2020)","DOI":"10.36227\/techrxiv.12476426"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Polsinelli, M., Cinque, L., Placidi, G.: A light CNN for detecting COVID-19 from CT scans of the chest. arXiv Prepr. arXiv2004.12837 (2020)","DOI":"10.1016\/j.patrec.2020.10.001"}],"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_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T20:16:29Z","timestamp":1619295389000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-1086-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811610851","9789811610868"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-1086-8_14","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)"}}]}}