{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:24:16Z","timestamp":1743006256945,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031116322"},{"type":"electronic","value":"9783031116339"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-11633-9_14","type":"book-chapter","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T19:03:55Z","timestamp":1658430235000},"page":"174-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["COV-XDCNN: Deep Learning Model with External Filter for Detecting COVID-19 on Chest X-Rays"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9867-3796","authenticated-orcid":false,"given":"Arnab","family":"Dey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"volume-title":"Medical imaging: technology and applications","year":"2017","unstructured":"Farncombe, T., Iniewski, K. (eds.): Medical imaging: technology and applications. CRC Press, Boca Raton (2017)","key":"14_CR1"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"113909","DOI":"10.1016\/j.eswa.2020.113909","volume":"165","author":"TB Chandra","year":"2021","unstructured":"Chandra, T.B., Verma, K., Singh, B.K., Jain, D., Netam, S.S.: Coronavirus disease (COVID-19) detection in chest X-ray images using majority voting based classifier ensemble. Expert Syst. Appl. 165, 113909 (2021)","journal-title":"Expert Syst. Appl."},{"doi-asserted-by":"crossref","unstructured":"Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Mirjalili, S., Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. In: Multimodal Image Exploitation and Learning 2021, vol. 11734, p. 117340E. International Society for Optics and Photonics (2021)","key":"14_CR3","DOI":"10.1117\/12.2588672"},{"issue":"2","key":"14_CR4","first-page":"01","volume":"2","author":"N Khan","year":"2020","unstructured":"Khan, N., Ullah, F., Hassan, M.A., Hussain, A.: COVID-19 classification based on Chest X-Ray images using machine learning techniques. J. Comput. Sci. Technol. Stud. 2(2), 01\u201311 (2020)","journal-title":"J. Comput. Sci. Technol. Stud."},{"issue":"10223","key":"14_CR5","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. The Lancet 395(10223), 497\u2013506 (2020)","journal-title":"The Lancet"},{"issue":"1","key":"14_CR6","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10489-020-01826-w","volume":"51","author":"S Ahuja","year":"2020","unstructured":"Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K.: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl. Intell. 51(1), 571\u2013585 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01826-w","journal-title":"Appl. Intell."},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan, A.I., Shah, J.L., Bhat, M.M.: CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput. Methods Programs Biomed. 196, 105581 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"14_CR8","doi-asserted-by":"publisher","first-page":"657","DOI":"10.14245\/ns.1938396.198","volume":"16","author":"M Kim","year":"2019","unstructured":"Kim, M., et al.: Deep learning in medical imaging. Neurospine 16(4), 657 (2019)","journal-title":"Neurospine"},{"issue":"10","key":"14_CR9","doi-asserted-by":"publisher","first-page":"e0257884","DOI":"10.1371\/journal.pone.0257884","volume":"16","author":"LR Baltazar","year":"2021","unstructured":"Baltazar, L.R., et al.: Artificial intelligence on COVID-19 pneumonia detection using chest xray images. PLoS ONE 16(10), e0257884 (2021)","journal-title":"PLoS ONE"},{"doi-asserted-by":"crossref","unstructured":"Chung, M., et al.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202\u2013207, 26 (2020)","key":"14_CR10","DOI":"10.1148\/radiol.2020200230"},{"issue":"8","key":"14_CR11","doi-asserted-by":"publisher","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","volume":"31","author":"S Wang","year":"2021","unstructured":"Wang, S., et al.: A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). Eur. Radiol. 31(8), 6096\u20136104 (2021)","journal-title":"Eur. Radiol."},{"doi-asserted-by":"crossref","unstructured":"Wang, W., Li, Y., Zou, T., Wang, X., You, J., Luo, Y.: A novel image classification approach via dense-MobileNet models. Mobile Information Systems (2020)","key":"14_CR12","DOI":"10.1155\/2020\/7602384"},{"unstructured":"Cohen, J. P., Morrison, P., Dao, L.: COVID-19 image data collection (2020). arXiv preprint arXiv:2003.11597","key":"14_CR13"},{"doi-asserted-by":"crossref","unstructured":"Alqudah, A.M., Qazan, S., Alqudah, A.: Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks (2020)","key":"14_CR14","DOI":"10.21203\/rs.3.rs-24305\/v1"},{"doi-asserted-by":"crossref","unstructured":"Hall, L.O., Paul, R., Goldgof, D.B., Goldgof, G.M.: Finding covid-19 from chest x-rays using deep learning on a small dataset (2020). arXiv preprint arXiv:2004.02060","key":"14_CR15","DOI":"10.36227\/techrxiv.12083964.v3"},{"doi-asserted-by":"publisher","unstructured":"Asraf, A., Islam, Z.: COVID19, Pneumonia and Normal Chest X-ray PA Dataset. Mendeley Data, V1 (2021). https:\/\/doi.org\/10.17632\/jctsfj2sfn.1","key":"14_CR16","DOI":"10.17632\/jctsfj2sfn.1"},{"unstructured":"Mangal, A., et al.: CovidAID: COVID-19 detection using chest X-ray (2020). arXiv preprint arXiv:2004.09803","key":"14_CR17"},{"doi-asserted-by":"crossref","unstructured":"Luz, E., Silva, P.L., Silva, R., Silva, L., Moreira, G., Menotti, D.: Towards an effective and efficient deep learning model for covid19 patterns detection in x-ray images (2020). arXiv:2004.05717","key":"14_CR18","DOI":"10.1007\/s42600-021-00151-6"},{"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). arXiv preprint arXiv:2003.11055","key":"14_CR19"},{"unstructured":"Ilyas, M., Rehman, H., Nait-ali, A.: Detection of Covid-19 from chest X-ray images using artificial intelligence: an early review (2020). arXiv preprint arXiv:2004.05436","key":"14_CR20"},{"key":"14_CR21","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.clinimag.2020.04.001","volume":"64","author":"A Jacobi","year":"2020","unstructured":"Jacobi, A., Chung, M., Bernheim, A., Eber, C.: Portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clin. Imaging 64, 35\u201342 (2020)","journal-title":"Clin. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Tsai, E.B., et al.: The RSNA international COVID-19 open radiology database (RICORD). Radiology 299(1), E204\u2013E213 (2021)","key":"14_CR22","DOI":"10.1148\/radiol.2021203957"},{"issue":"4","key":"14_CR23","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s12539-020-00393-5","volume":"12","author":"R Zhang","year":"2020","unstructured":"Zhang, R., et al.: COVID19XrayNet: a two-step transfer learning model for the COVID-19 detecting problem based on a limited number of chest X-Ray images. Interdiscip. Sci. Comput. Life Sci. 12(4), 555\u2013565 (2020). https:\/\/doi.org\/10.1007\/s12539-020-00393-5","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"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 preprint arXiv:2006.11988","key":"14_CR24"},{"key":"14_CR25","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury, M.E.H., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665\u2013132676 (2020)","journal-title":"IEEE Access"},{"key":"14_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-319-40663-3_2","volume-title":"Advances in Neural Networks \u2013 ISNN 2016","author":"R Wang","year":"2016","unstructured":"Wang, R.: Edge detection using convolutional neural network. In: Cheng, L., Liu, Q., Ronzhin, A. (eds.) Advances in Neural Networks \u2013 ISNN 2016. LNCS, vol. 9719, pp. 12\u201320. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-40663-3_2"},{"key":"14_CR27","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, 1122-1131.e9 (2018)","journal-title":"Cell"},{"unstructured":"Deng, R.O., et al.: ImageNet large scale visual recognition challenge (2015). arXiv preprint arXiv:14090575","key":"14_CR28"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980","key":"14_CR29"}],"container-title":["IFIP Advances in Information and Communication Technology","Computer, Communication, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-11633-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T19:07:02Z","timestamp":1658430422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-11633-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031116322","9783031116339"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-11633-9_14","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCSP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer, Communication, and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chennai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 February 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icccsp2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icccsp.com\/2022\/","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":"111","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":"21","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":"2","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":"19% - 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":"3","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":"Conference was held virtually due to COVID-19 pandemic.","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)"}}]}}