{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:59:43Z","timestamp":1772233183352,"version":"3.50.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031399640","type":"print"},{"value":"9783031399657","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\/978-3-031-39965-7_2","type":"book-chapter","created":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T16:01:37Z","timestamp":1692547297000},"page":"14-25","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Fuzzy Cognitive Map Learning Approach for Coronary Artery Disease Diagnosis in Nuclear Medicine"],"prefix":"10.1007","author":[{"given":"Anna","family":"Feleki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6439-9282","authenticated-orcid":false,"given":"Ioannis D.","family":"Apostolopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Papageorgiou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2498-9661","authenticated-orcid":false,"given":"Elpiniki I.","family":"Papageorgiou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris J.","family":"Apostolopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5416-1991","authenticated-orcid":false,"given":"Nikolaos I.","family":"Papandrianos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"611","DOI":"10.2215\/CJN.03871106","volume":"2","author":"PA McCullough","year":"2007","unstructured":"McCullough, P.A.: Coronary artery disease. Clin. J. Am. Soc. Nephrol. 2, 611 (2007). https:\/\/doi.org\/10.2215\/CJN.03871106","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"6362","DOI":"10.3390\/app11146362","volume":"11","author":"N Papandrianos","year":"2021","unstructured":"Papandrianos, N., Papageorgiou, E.: Automatic diagnosis of coronary artery disease in SPECT myocardial perfusion imaging employing deep learning. Appl. Sci. 11, 6362 (2021). https:\/\/doi.org\/10.3390\/app11146362","journal-title":"Appl. Sci."},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1967\/s002449912101","volume":"23","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos, I.D., Papathanasiou, N.D., Spyridonidis, T., Apostolopoulos, D.J.: Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation. Hell. J. Nucl. Med. 23, 125\u2013132 (2020). https:\/\/doi.org\/10.1967\/s002449912101","journal-title":"Hell. J. Nucl. Med."},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.media.2017.11.008","volume":"44","author":"M Zreik","year":"2018","unstructured":"Zreik, M., et al.: Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis. Med. Image Anal. 44, 72\u201385 (2018). https:\/\/doi.org\/10.1016\/j.media.2017.11.008","journal-title":"Med. Image Anal."},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s12149-022-01762-4","volume":"36","author":"NI Papandrianos","year":"2022","unstructured":"Papandrianos, N.I., Apostolopoulos, I.D., Feleki, A., Apostolopoulos, D.J., Papageorgiou, E.I.: Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease. Ann. Nucl. Med. 36, 823\u2013833 (2022). https:\/\/doi.org\/10.1007\/s12149-022-01762-4","journal-title":"Ann. Nucl. Med."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"7592","DOI":"10.3390\/app12157592","volume":"12","author":"NI Papandrianos","year":"2022","unstructured":"Papandrianos, N.I., Feleki, A., Moustakidis, S., Papageorgiou, E.I., Apostolopoulos, I.D., Apostolopoulos, D.J.: An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and grad-CAM. Appl. Sci. 12, 7592 (2022). https:\/\/doi.org\/10.3390\/app12157592","journal-title":"Appl. Sci."},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1080\/10255842.2020.1768534","volume":"23","author":"ID Apostolopoulos","year":"2020","unstructured":"Apostolopoulos, I.D., Groumpos, P.P.: Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps. Comput. Methods Biomech. Biomed. Eng. 23, 879\u2013887 (2020). https:\/\/doi.org\/10.1080\/10255842.2020.1768534","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"2_CR8","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1016\/j.asoc.2007.06.006","volume":"8","author":"EI Papageorgiou","year":"2008","unstructured":"Papageorgiou, E.I., et al.: Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl. Soft Comput. 8, 820\u2013828 (2008). https:\/\/doi.org\/10.1016\/j.asoc.2007.06.006","journal-title":"Appl. Soft Comput."},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Nasiriyan-Rad, H., Amirkhani, A., Naimi, A., Mohammadi, K.: Learning fuzzy cognitive map with PSO algorithm for grading celiac disease. In: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), pp. 341\u2013346 (2016). https:\/\/doi.org\/10.1109\/ICBME.2016.7890984","DOI":"10.1109\/ICBME.2016.7890984"},{"key":"2_CR10","doi-asserted-by":"publisher","first-page":"3798","DOI":"10.1016\/j.asoc.2012.03.064","volume":"12","author":"EI Papageorgiou","year":"2012","unstructured":"Papageorgiou, E.I., Kannappan, A.: Fuzzy cognitive map ensemble learning paradigm to solve classification problems: application to autism identification. Appl. Soft Comput. 12, 3798\u20133809 (2012). https:\/\/doi.org\/10.1016\/j.asoc.2012.03.064","journal-title":"Appl. Soft Comput."},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Papageorgiou, E.I., Papandrianos, N.I., Apostolopoulos, D.J., Vassilakos, P.J.: Fuzzy Cognitive Map based decision support system for thyroid diagnosis management. In: 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), pp. 1204\u20131211 (2008). https:\/\/doi.org\/10.1109\/FUZZY.2008.4630524","DOI":"10.1109\/FUZZY.2008.4630524"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"104069","DOI":"10.1016\/j.engappai.2020.104069","volume":"97","author":"O Carvajal","year":"2021","unstructured":"Carvajal, O., Melin, P., Miramontes, I., Prado-Arechiga, G.: Optimal design of a general type-2 fuzzy classifier for the pulse level and its hardware implementation. Eng. Appl. Artif. Intell. 97, 104069 (2021). https:\/\/doi.org\/10.1016\/j.engappai.2020.104069","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3390\/axioms8010008","volume":"8","author":"JC Guzm\u00e1n","year":"2019","unstructured":"Guzm\u00e1n, J.C., Miramontes, I., Melin, P., Prado-Arechiga, G.: Optimal genetic design of type-1 and interval type-2 fuzzy systems for blood pressure level classification. Axioms 8, 8 (2019). https:\/\/doi.org\/10.3390\/axioms8010008","journal-title":"Axioms"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/axioms11090485","volume":"11","author":"I Miramontes","year":"2022","unstructured":"Miramontes, I., Melin, P.: Interval type-2 fuzzy approach for dynamic parameter adaptation in the bird swarm algorithm for the optimization of fuzzy medical classifier. Axioms 11, 485 (2022). https:\/\/doi.org\/10.3390\/axioms11090485","journal-title":"Axioms"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1007\/s10729-022-09611-6","volume":"25","author":"W Hoyos","year":"2022","unstructured":"Hoyos, W., Aguilar, J., Toro, M.: A clinical decision-support system for dengue based on fuzzy cognitive maps. Health Care Manag. Sci. 25, 666\u2013681 (2022). https:\/\/doi.org\/10.1007\/s10729-022-09611-6","journal-title":"Health Care Manag. Sci."},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/S0020-7373(86)80040-2","volume":"24","author":"B Kosko","year":"1986","unstructured":"Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24, 65\u201375 (1986). https:\/\/doi.org\/10.1016\/S0020-7373(86)80040-2","journal-title":"Int. J. Man-Mach. Stud."},{"key":"2_CR17","doi-asserted-by":"publisher","unstructured":"Sovatzidi, G., Vasilakakis, M.D., Iakovidis, D.K.: IF3: an interpretable feature fusion framework for lesion risk assessment based on auto-constructed fuzzy cognitive maps. In: Ali, S., van der Sommen, F., Papie\u017c, B.W., van Eijnatten, M., Jin, Y., Kolenbrander, I. (eds.) CaPTion 2022. LNCS, vol. 13581, pp. 77\u201386. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17979-2_8","DOI":"10.1007\/978-3-031-17979-2_8"},{"issue":"2","key":"2_CR18","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","volume":"22","author":"D Wang","year":"2017","unstructured":"Wang, D., Tan, D., Liu, L.: Particle swarm optimization algorithm: an overview. Soft. Comput. 22(2), 387\u2013408 (2017). https:\/\/doi.org\/10.1007\/s00500-016-2474-6","journal-title":"Soft. Comput."},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"105659","DOI":"10.1016\/j.cmpb.2020.105659","volume":"196","author":"JB Raja","year":"2020","unstructured":"Raja, J.B., Pandian, S.C.: PSO-FCM based data mining model to predict diabetic disease. Comput. Methods Programs Biomed. 196, 105659 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105659","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Lecture Notes in Computer Science","Fuzzy Logic and Technology, and Aggregation Operators"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39965-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T16:01:46Z","timestamp":1692547306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39965-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031399640","9783031399657"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39965-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EUSFLAT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference of the European Society for Fuzzy Logic and Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palma de Mallorca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"4 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eusflat2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.eusflat2023.eu\/","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":"161","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":"71","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":"44% - 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":"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)"}}]}}