{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:02:07Z","timestamp":1742925727657,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031581731"},{"type":"electronic","value":"9783031581748"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-58174-8_50","type":"book-chapter","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:02:39Z","timestamp":1719910959000},"page":"603-611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CED-Net: A Generalized Deep Wide Model for\u00a0Covid Detection"],"prefix":"10.1007","author":[{"given":"Shivani Manoj","family":"Toshniwal","sequence":"first","affiliation":[]},{"given":"P.","family":"Pranav","sequence":"additional","affiliation":[]},{"given":"M. N.","family":"Toshniwal","sequence":"additional","affiliation":[]},{"given":"M.","family":"Srinivas","sequence":"additional","affiliation":[]},{"given":"P. Radha","family":"Krishna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,3]]},"reference":[{"key":"50_CR1","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale, arXiv preprintarXiv:2010.11929 (2020)"},{"key":"50_CR2","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: Cmt: convolutional neural networks meet vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.\u00a012175\u201312185 (2022)","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"50_CR3","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp.\u00a06105\u20136114. PMLR (2019)"},{"key":"50_CR4","doi-asserted-by":"publisher","first-page":"110050","DOI":"10.1016\/j.chaos.2020.110050","volume":"139","author":"M Yadav","year":"2020","unstructured":"Yadav, M., Perumal, M., Srinivas, M.: Analysis on novel coronavirus (covid-19) using machine learning methods. Chaos, Solitons Fractals 139, 110050 (2020)","journal-title":"Chaos, Solitons Fractals"},{"key":"50_CR5","doi-asserted-by":"crossref","unstructured":"Fan, Z., Jamil, M., Sadiq, M.T., Huang, X., Yu, X.: Exploiting multiple optimizers with transfer learning techniques for the identification of covid-19 patients. J. Healthcare Eng. 2020, 13 (2020)","DOI":"10.1155\/2020\/8889412"},{"issue":"9","key":"50_CR6","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.1109\/TII.2021.3057683","volume":"17","author":"S Tang","year":"2021","unstructured":"Tang, S., et al.: EDL-covid: Ensemble deep learning for covid-19 case detection from chest x-ray images. IEEE Trans. Industr. Inf. 17(9), 6539\u20136549 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"50_CR7","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,\u2019 arXiv preprintarXiv:2003.11055 (2020)"},{"issue":"21","key":"50_CR8","doi-asserted-by":"publisher","first-page":"11086","DOI":"10.3390\/ijerph182111086","volume":"18","author":"D Shome","year":"2021","unstructured":"Shome, D., et al.: Covid-transformer: interpretable covid-19 detection using vision transformer for healthcare. Int. J. Environ. Res. Public Health 18(21), 11086 (2021)","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"1","key":"50_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-27266-9","volume":"13","author":"Z Ullah","year":"2023","unstructured":"Ullah, Z., Usman, M., Latif, S., Gwak, J.: Densely attention mechanism based network for covid-19 detection in chest x-rays. Sci. Rep. 13(1), 1\u201314 (2023)","journal-title":"Sci. Rep."},{"key":"50_CR10","doi-asserted-by":"crossref","unstructured":"Sharma, V., Dyreson, C.: Covid-19 screening using residual attention network an artificial intelligence approach. In: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.\u00a01354\u20131361 (2020)","DOI":"10.1109\/ICMLA51294.2020.00211"},{"issue":"5","key":"50_CR11","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1007\/s10489-020-02055-x","volume":"51","author":"C Sitaula","year":"2021","unstructured":"Sitaula, C., Hossain, M.B.: Attention-based VGG-16 model for covid-19 chest x-ray image classification. Appl. Intell. 51(5), 2850\u20132863 (2021)","journal-title":"Appl. Intell."},{"key":"50_CR12","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.\u00a0770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"50_CR13","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar, F., Korkmaz, D.: Covidiagnosis-net: Deep bayes-squeezenet based diagnosis of the coronavirus disease 2019 (covid-19) from x-ray images. Med. Hypotheses 140, 109761 (2020)","journal-title":"Med. Hypotheses"},{"key":"50_CR14","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","volume":"138","author":"P Afshar","year":"2020","unstructured":"Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K.N., Mohammadi, A.: Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recogn. Lett. 138, 638\u2013643 (2020)","journal-title":"Pattern Recogn. Lett."},{"issue":"1","key":"50_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","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), 1\u201312 (2020)","journal-title":"Sci. Rep."},{"key":"50_CR16","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."},{"key":"50_CR17","doi-asserted-by":"publisher","unstructured":"Anila Glory, H., Meghana, S., Kesav Kumar, J.S., Shankar Sriram, V.S.: Stacked dark covid-net: a multi-class multi-label classification approach for diagnosing COVID-19 using chest x-ray images. In: Santosh, K., Hegadi, R., Pal, U. (eds.) Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021, CCIS, vol. 1576, pp. 61\u201375. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-07005-1_7","DOI":"10.1007\/978-3-031-07005-1_7"},{"key":"50_CR18","doi-asserted-by":"crossref","unstructured":"Qu, R., Yang, Y., Wang, Y.: Covid-19 detection using CT image based on yolov5 network. In: 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), pp.\u00a0622\u2013625. IEEE (2021)","DOI":"10.1109\/IAECST54258.2021.9695714"},{"key":"50_CR19","doi-asserted-by":"crossref","unstructured":"Nugraha, D.A.T., Nasution, A.M.: Comparison of texture feature extraction method for covid-19 detection with deep learning. In: 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), pp.\u00a0393\u2013397 (2022)","DOI":"10.1109\/CyberneticsCom55287.2022.9865582"},{"key":"50_CR20","doi-asserted-by":"crossref","unstructured":"Chollet, F., et al.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"50_CR21","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":"50_CR22","unstructured":"Ahemateja, K.: Covid x-ray dataset (2021). https:\/\/www.kaggle.com\/datasets\/ahemat eja19bec1025\/covid-xray-dataset"}],"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\/978-3-031-58174-8_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:06:38Z","timestamp":1719911198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-58174-8_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031581731","9783031581748"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-58174-8_50","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 July 2024","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":"Jammu","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cvip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iitjammu.ac.in\/cvip2023\/","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":"Online CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"461","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":"140","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":"30% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}