{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:57:23Z","timestamp":1777658243145,"version":"3.51.4"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031346187","type":"print"},{"value":"9783031346194","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34619-4_13","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:01:31Z","timestamp":1686423691000},"page":"152-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Convolutional Neural Network Model to\u00a0Detect COVID-19 Patients Utilizing Chest X-Ray Images"],"prefix":"10.1007","author":[{"given":"Md. Shahriare","family":"Satu","sequence":"first","affiliation":[]},{"given":"Khair","family":"Ahammed","sequence":"additional","affiliation":[]},{"given":"Mohammad Zoynul","family":"Abedin","sequence":"additional","affiliation":[]},{"given":"Md. Auhidur","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Sheikh Mohammed Shariful","family":"Islam","sequence":"additional","affiliation":[]},{"given":"A. K. M.","family":"Azad","sequence":"additional","affiliation":[]},{"given":"Salem A.","family":"Alyami","sequence":"additional","affiliation":[]},{"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/s10489-020-01829-7","volume":"51","author":"A Abbas","year":"2020","unstructured":"Abbas, A., Abdelsamea, M.M., Gaber, M.M.: Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 51, 854\u2013864 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01829-7","journal-title":"Appl. Intell."},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Ahammed, K., Satu, M.S., Khan, M.I., Whaiduzzaman, M.: Predicting infectious state of hepatitis C virus affected patient\u2019s applying machine learning methods. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1371\u20131374. IEEE (2020)","DOI":"10.1109\/TENSYMP50017.2020.9230464"},{"key":"13_CR3","doi-asserted-by":"crossref","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. 1 (2020)","DOI":"10.1007\/s13246-020-00865-4"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Butt, C., Gill, J., Chun, D., Babu, B.A.: Deep learning system to screen coronavirus disease 2019 pneumonia. Appl. Intell. 1 (2020)","DOI":"10.1007\/s10489-020-01714-3"},{"key":"13_CR5","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1016\/j.eswa.2020.113909, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417420307041","DOI":"10.1016\/j.eswa.2020.113909"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Chowdhury, M.E., et al.: Can AI help in screening viral and COVID-19 pneumonia? arXiv preprint arXiv:2003.13145 (2020)","DOI":"10.1109\/ACCESS.2020.3010287"},{"key":"13_CR7","unstructured":"Cohen, J.P., Morrison, P., Dao, L.: COVID-19 image data collection. arXiv:2003.11597 (2020). https:\/\/github.com\/ieee8023\/covid-chestxray-dataset"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Duran-Lopez, L., Dominguez-Morales, J.P., Corral-Jaime, J., Vicente-Diaz, S., Linares-Barranco, A.: COVID-XNet: a custom deep learning system to diagnose and locate COVID-19 in chest X-ray images. Appl. Sci. 10(16), 5683 (2020). https:\/\/doi.org\/10.3390\/app10165683, https:\/\/www.mdpi.com\/2076-3417\/10\/16\/5683","DOI":"10.3390\/app10165683"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Dutta, S., Bandyopadhyay, S.K., Kim, T.H.: CNN-LSTM model for verifying predictions of COVID-19 cases. Asian J. Res. Comput. Sci. 25\u201332 (2020). https:\/\/doi.org\/10.9734\/ajrcos\/2020\/v5i430141, https:\/\/www.journalajrcos.com\/index.php\/AJRCOS\/article\/view\/30141","DOI":"10.9734\/ajrcos\/2020\/v5i430141"},{"key":"13_CR10","doi-asserted-by":"publisher","unstructured":"Heidari, M., Mirniaharikandehei, S., Khuzani, A.Z., Danala, G., Qiu, Y., Zheng, B.: Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int. J. Med. Inform. 144, 104284 (2020). https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104284, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S138650562030959X","DOI":"10.1016\/j.ijmedinf.2020.104284"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Holshue, M.L., et al.: First case of 2019 novel coronavirus in the United States. New Engl. J. Med. (2020)","DOI":"10.1056\/NEJMoa2001191"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Ismael, A.M., \u015eeng\u00fcr, A.: Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst. Appl. 164, 114054 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114054, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417420308198","DOI":"10.1016\/j.eswa.2020.114054"},{"key":"13_CR13","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40747-020-00199-4","volume":"7","author":"ME Karar","year":"2020","unstructured":"Karar, M.E., Hemdan, E.E.D., Shouman, M.A.: Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex Intell. Syst. 7, 235\u2013247 (2020). https:\/\/doi.org\/10.1007\/s40747-020-00199-4","journal-title":"Complex Intell. Syst."},{"key":"13_CR14","doi-asserted-by":"publisher","unstructured":"Karthik, R., Menaka, R., M., H.: Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN. Appl. Soft Comput. 106744 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106744, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494620306827","DOI":"10.1016\/j.asoc.2020.106744"},{"key":"13_CR15","doi-asserted-by":"publisher","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 Program. Biomed. 196, 105581 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105581, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0169260720314140","DOI":"10.1016\/j.cmpb.2020.105581"},{"issue":"3","key":"13_CR16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1097\/RTI.0000000000000404","volume":"34","author":"LJ Kroft","year":"2019","unstructured":"Kroft, L.J., van der Velden, L., Gir\u00f3n, I.H., Roelofs, J.J., de Roos, A., Geleijns, J.: Added value of ultra-low-dose computed tomography, dose equivalent to chest x-ray radiography, for diagnosing chest pathology. J. Thorac. Imaging 34(3), 179 (2019)","journal-title":"J. Thorac. Imaging"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Lippi, G., Plebani, M.: Procalcitonin in patients with severe coronavirus disease 2019 (covid-19): a meta-analysis. Clin. Chimica Acta Int. J. Clin. Chem. 505, 190 (2020)","DOI":"10.1016\/j.cca.2020.03.004"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Lu, R., et al.: Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395(10224), 565\u2013574 (2020)","DOI":"10.1016\/S0140-6736(20)30251-8"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., Jamalipour Soufi, G.: Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med. Image Anal. 65, 101794 (2020). https:\/\/doi.org\/10.1016\/j.media.2020.101794, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841520301584","DOI":"10.1016\/j.media.2020.101794"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"195594","DOI":"10.1109\/ACCESS.2020.3033762","volume":"8","author":"JD Moura","year":"2020","unstructured":"Moura, J.D., et al.: Deep convolutional approaches for the analysis of COVID-19 using chest X-ray images from portable devices. IEEE Access 8, 195594\u2013195607 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3033762","journal-title":"IEEE Access"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Ng, M.Y., et al.: Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol. Cardiothorac. Imaging 2(1), e200034 (2020)","DOI":"10.1148\/ryct.2020200034"},{"issue":"1","key":"13_CR22","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/JAS.2020.1003393","volume":"8","author":"EF Ohata","year":"2021","unstructured":"Ohata, E.F., et al.: Automatic detection of COVID-19 infection using chest X-ray images through transfer learning. IEEE\/CAA J. Autom. Sinica 8(1), 239\u2013248 (2021). https:\/\/doi.org\/10.1109\/JAS.2020.1003393","journal-title":"IEEE\/CAA J. Autom. Sinica"},{"key":"13_CR23","unstructured":"World Health Organization, et al.: Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases: interim guidance, 2 March 2020. Technical report, World Health Organization (2020)"},{"issue":"5","key":"13_CR24","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1108\/IJPCC-06-2020-0060","volume":"16","author":"MK Pandit","year":"2020","unstructured":"Pandit, M.K., Banday, S.A.: SARS n-CoV2-19 detection from chest x-ray images using deep neural networks. Int. J. Pervasive Comput. Commun. 16(5), 419\u2013427 (2020). https:\/\/doi.org\/10.1108\/IJPCC-06-2020-0060","journal-title":"Int. J. Pervasive Comput. Commun."},{"key":"13_CR25","series-title":"Algorithms for Intelligent Systems","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/978-981-15-3607-6_36","volume-title":"Proceedings of International Joint Conference on Computational Intelligence","author":"M Shahriare Satu","year":"2020","unstructured":"Shahriare Satu, M., Atik, S.T., Moni, M.A.: A novel hybrid machine learning model to predict diabetes mellitus. In: Uddin, M.S., Bansal, J.C. (eds.) Proceedings of International Joint Conference on Computational Intelligence. AIS, pp. 453\u2013465. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-3607-6_36"},{"key":"13_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-59277-6_27","volume-title":"Brain Informatics","author":"MS Satu","year":"2020","unstructured":"Satu, M.S., Rahman, S., Khan, M.I., Abedin, M.Z., Kaiser, M.S., Mahmud, M.: Towards improved detection of cognitive performance using bidirectional multilayer long-short term memory neural network. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 297\u2013306. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59277-6_27"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Sekeroglu, B., Ozsahin, I.: Detection of COVID-19 from chest X-ray images using convolutional neural networks. SLAS TECHNOL.: Transl. Life Sci. Innov. 25(6), 553\u2013565 (2020). https:\/\/doi.org\/10.1177\/2472630320958376","DOI":"10.1177\/2472630320958376"},{"key":"13_CR28","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1007\/s40747-020-00216-6","volume":"7","author":"K Shankar","year":"2020","unstructured":"Shankar, K., Perumal, E.: A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images. Complex Intell. Syst. 7, 1277\u20131293 (2020). https:\/\/doi.org\/10.1007\/s40747-020-00216-6","journal-title":"Complex Intell. Syst."},{"key":"13_CR29","doi-asserted-by":"publisher","unstructured":"Shorfuzzaman, M., Hossain, M.S.: MetaCOVID: a siamese neural network framework with contrastive loss for N-shot diagnosis of COVID-19 patients. Pattern Recognit. 107700 (2020). https:\/\/doi.org\/10.1016\/j.patcog.2020.107700, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0031320320305033","DOI":"10.1016\/j.patcog.2020.107700"},{"issue":"6","key":"13_CR30","first-page":"2000094","volume":"25","author":"SB Stoecklin","year":"2020","unstructured":"Stoecklin, S.B., et al.: First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020. Eurosurveillance 25(6), 2000094 (2020)","journal-title":"Eurosurveillance"},{"key":"13_CR31","doi-asserted-by":"publisher","unstructured":"Wang, L., Lin, Z.Q., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10(1), 19549 (2020). https:\/\/doi.org\/10.1038\/s41598-020-76550-z, https:\/\/www.nature.com\/articles\/s41598-020-76550-z","DOI":"10.1038\/s41598-020-76550-z"},{"key":"13_CR32","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","volume":"51","author":"T Zebin","year":"2020","unstructured":"Zebin, T., Rezvy, S.: COVID-19 detection and disease progression visualization: deep learning on chest X-rays for classification and coarse localization. Appl. Intell. 51, 1010\u20131021 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01867-1","journal-title":"Appl. Intell."},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, N., et al.: A novel coronavirus from patients with pneumonia in China, 2019. New Engl. J. Med. (2020)","DOI":"10.1056\/NEJMoa2001017"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Machine Intelligence and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34619-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T19:06:27Z","timestamp":1686423987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34619-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346187","9783031346194"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34619-4_13","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Intelligence and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Noakhali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangladesh","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":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/confmiet.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"272","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":"104","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":"2","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","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)"}}]}}