{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:08:49Z","timestamp":1743077329645,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030850296"},{"type":"electronic","value":"9783030850302"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-85030-2_46","type":"book-chapter","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T12:03:05Z","timestamp":1629460985000},"page":"559-569","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Models for Covid Prediction in\u00a0X-Ray Images"],"prefix":"10.1007","author":[{"given":"Juan Carlos","family":"Morales Vega","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Carrillo-Perez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas","family":"Toledano Pav\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis Javier","family":"Herrera Maldonado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignacio","family":"Rojas Ruiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,21]]},"reference":[{"issue":"1","key":"46_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Chauhan, R., Ghanshala, K., Joshi, R.: Convolutional neural network (CNN) for image detection and recognition, pp. 278\u2013282, 2018","DOI":"10.1109\/ICSCCC.2018.8703316"},{"key":"46_CR3","doi-asserted-by":"publisher","first-page":"46450","DOI":"10.1038\/srep46450","volume":"7","author":"A Cruz-Roa","year":"2017","unstructured":"Cruz-Roa, A., et al.: Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017)","journal-title":"Sci. Rep."},{"key":"46_CR4","unstructured":"Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks (2017)"},{"key":"46_CR5","unstructured":"Vay\u00e1, M.I., et al.: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients (2020)"},{"key":"46_CR6","doi-asserted-by":"publisher","first-page":"01","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., Kuprel, B., Novoa, R., Ko, J., Swetter, S., Blau, H.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 01 (2017)","journal-title":"Nature"},{"key":"46_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/3-540-44673-7_12","volume-title":"Machine Learning and Its Applications","author":"T Evgeniou","year":"2001","unstructured":"Evgeniou, T., Pontil, M.: Support vector machines: theory and applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 249\u2013257. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44673-7_12"},{"key":"46_CR8","doi-asserted-by":"publisher","unstructured":"Gianchandani, N., Jaiswal, A., Singh, D., Kumar, V., Kaur, M.: Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images. J. Ambient Intell. Hum. Comput., 1\u201313 (2020). https:\/\/doi.org\/10.1007\/s12652-020-02669-6","DOI":"10.1007\/s12652-020-02669-6"},{"key":"46_CR9","first-page":"09","volume":"413","author":"B Giri","year":"2020","unstructured":"Giri, B., Pandey, S., Shrestha, R., Pokharel, K., Ligler, F., Neupane, B.: Review of analytical performance of COVID-19 detection methods. Anal. Bioanal. Chem. 413, 09 (2020)","journal-title":"Anal. Bioanal. Chem."},{"key":"46_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/978-3-540-39964-3_62","volume-title":"On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE","author":"G Guo","year":"2003","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., Greer, K.: KNN model-based approach in classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 986\u2013996. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39964-3_62"},{"issue":"10","key":"46_CR11","doi-asserted-by":"publisher","first-page":"2133","DOI":"10.1080\/01431169108955241","volume":"12","author":"LJ Guo","year":"1991","unstructured":"Guo, L.J.: Balance contrast enhancement technique and its application in image colour composition. Int. J. Remote Sens. 12(10), 2133\u20132151 (1991)","journal-title":"Int. J. Remote Sens."},{"key":"46_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-10578-9_23","volume-title":"Computer Vision \u2013 ECCV 2014","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346\u2013361. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10578-9_23"},{"key":"46_CR13","doi-asserted-by":"publisher","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","volume":"8","author":"S Hu","year":"2020","unstructured":"Hu, S., et al.: Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8, 118869\u2013118883 (2020)","journal-title":"IEEE Access"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR arXiv:1608.06993 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"46_CR15","doi-asserted-by":"publisher","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","volume":"142","author":"E Hussain","year":"2021","unstructured":"Hussain, E., Hasan, M., Rahman, M.A., Lee, I., Tamanna, T., Parvez, M.Z.: CoroDet: a deep learning based classification for COVID-19 detection using chest x-ray images. Chaos, Solitons Fractals 142, 110495 (2021)","journal-title":"Chaos, Solitons Fractals"},{"key":"46_CR16","unstructured":"Islam, J., Zhang, Y.: Towards robust lung segmentation in chest radiographs with deep learning (2018)"},{"key":"46_CR17","first-page":"01","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Neural Inf. Process. Syst. 25, 01 (2012)","journal-title":"Neural Inf. Process. Syst."},{"key":"46_CR18","first-page":"01","volume":"1","author":"S Mishra","year":"2017","unstructured":"Mishra, S., et al.: Principal component analysis. Int. J. Livestock Res. 1, 01 (2017)","journal-title":"Int. J. Livestock Res."},{"key":"46_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"K Oh","year":"2019","unstructured":"Oh, K., Chung, Y.C., Kim, K.W., Kim, W.S., Oh, I.S.: Classification and visualization of Alzheimer\u2019s disease using volumetric convolutional neural network and transfer learning. Sci. Rep. 9, 1\u201316 (2019)","journal-title":"Sci. Rep."},{"key":"46_CR20","unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. CoRR arXiv:1711.05225 (2017)"},{"key":"46_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"46_CR22","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"39","author":"S Roy","year":"2020","unstructured":"Roy, S., et al.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 39(8), 2676\u20132687 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"46_CR23","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-CAM: why did you say that? Visual explanations from deep networks via gradient-based localization. CoRR arXiv:1610.02391 (2016)","DOI":"10.1109\/ICCV.2017.74"},{"key":"46_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S, Shlens, J., Wojna, ,Z.B.: Rethinking the inception architecture for computer vision (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"46_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-0273-z","volume":"3","author":"YX Tang","year":"2020","unstructured":"Tang, Y.X., et al.: Automated abnormality classification of chest radiographs using deep convolutional neural networks. NPJ Digit. Med. 3, 1\u20138 (2020)","journal-title":"NPJ Digit. Med."},{"key":"46_CR27","doi-asserted-by":"publisher","first-page":"169","DOI":"10.3233\/AIC-170729","volume":"30","author":"A Tharwat","year":"2017","unstructured":"Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A.E.: Linear discriminant analysis: a detailed tutorial. AI Commun. 30, 169\u2013190 (2017)","journal-title":"AI Commun."},{"key":"46_CR28","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"46_CR29","doi-asserted-by":"crossref","unstructured":"Varshni, D., Thakral, K., Agarwal, L., Nijhawan, R., Mittal, A.: Pneumonia detection using CNN based feature extraction. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1\u20137 (2019)","DOI":"10.1109\/ICECCT.2019.8869364"},{"key":"46_CR30","unstructured":"Wang, L.L., et al.: CORD-19: the COVID-19 open research dataset (2020)"},{"key":"46_CR31","doi-asserted-by":"publisher","first-page":"106885","DOI":"10.1016\/j.asoc.2020.106885","volume":"98","author":"T Zhou","year":"2021","unstructured":"Zhou, T., Lu, H., Yang, Z., Qiu, S., Huo, B., Dong, Y.: The ensemble deep learning model for novel COVID-19 on CT images. Appl. Soft Comput. 98, 106885 (2021)","journal-title":"Appl. Soft Comput."},{"key":"46_CR32","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K.: Contrast limited adaptive histogram equalization, pp. 474\u2013485. Academic Press Professional Inc., Cambridge (1994)","DOI":"10.1016\/B978-0-12-336156-1.50061-6"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85030-2_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T19:01:47Z","timestamp":1673118107000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85030-2_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030850296","9783030850302"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85030-2_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","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":"134","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":"85","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":"63% - 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":"2,8","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":"2,2","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)"}}]}}