{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:07:34Z","timestamp":1743152854536,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030179342"},{"type":"electronic","value":"9783030179359"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-17935-9_7","type":"book-chapter","created":{"date-parts":[[2019,4,30]],"date-time":"2019-04-30T12:49:05Z","timestamp":1556628545000},"page":"63-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Novel Four Stages Classification of Breast Cancer Using Infrared Thermal Imaging and a Deep Learning Model"],"prefix":"10.1007","author":[{"given":"Sebastien","family":"Mambou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5992-2574","authenticated-orcid":false,"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1218-501X","authenticated-orcid":false,"given":"Petra","family":"Maresova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9746-8459","authenticated-orcid":false,"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9664-1109","authenticated-orcid":false,"given":"Kamil","family":"Kuca","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,4,13]]},"reference":[{"key":"7_CR1","unstructured":"National Breast Cancer Foundation: Breast Cancer Facts. www.nationalbreastcancer.org. https:\/\/www.nationalbreastcancer.org\/breast-cancer-facts. Accessed 2016"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.3390\/s18092799","volume":"18","author":"S Mambou","year":"2018","unstructured":"Mambou, S., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.: Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors 18, 2799 (2018)","journal-title":"Sensors"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.media.2018.03.006","volume":"47","author":"H Azam","year":"2018","unstructured":"Azam, H., Erika, D., Andrik, R., Kate, H., Zwiggelaar, R.: Deep learning in mammography and breast histology, an overview and future trends. Med. Image Anal. 47, 45\u201367 (2018)","journal-title":"Med. Image Anal."},{"key":"7_CR4","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/jimaging4010014","volume":"4","author":"R Andrik","year":"2018","unstructured":"Andrik, R., Bryan, W., Philip, J., Hui, W., John, W.: Breast density classification using local quinary patterns with various neighbourhood topologies. J. Imaging 4, 14 (2018)","journal-title":"J. Imaging"},{"key":"7_CR5","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/978-3-319-76081-0_34","volume-title":"Modern Approaches for Intelligent Information and Database Systems","author":"S Mambou","year":"2018","unstructured":"Mambou, S., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.: Breast cancer detection using modern visual IT techniques. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. SCI, vol. 769, pp. 397\u2013407. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76081-0_34"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.cmpb.2015.09.014","volume":"123","author":"A Amina","year":"2016","unstructured":"Amina, A., Susan, H., Anthony, J.: Potentialities of steady-state and transient thermography in breast tumour depth detection: a numerical study. Comput. Methods Programs Biomed. 123, 68\u201380 (2016). ISSN 0169-2607","journal-title":"Comput. Methods Programs Biomed."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1007\/s00464-016-5007-6","volume":"31","author":"LSF Boogerd","year":"2016","unstructured":"Boogerd, L.S.F., et al.: Laparoscopic detection and resection of occult liver tumors of multiple cancer types using real-time near-infrared fluorescence guidance. Surg. Endosc. 31, 952\u2013961 (2016)","journal-title":"Surg. Endosc."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.1016\/j.ijheatmasstransfer.2017.01.086","volume":"108","author":"G Satish","year":"2017","unstructured":"Satish, G., Kandlikar, I.: Infrared imaging technology for breast cancer detection \u2013 current status. Int. J. Heat Mass Transf. 108, 2303\u20132320 (2017)","journal-title":"Int. J. Heat Mass Transf."},{"issue":"12","key":"7_CR9","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1007\/s00595-015-1158-7","volume":"45","author":"T Namikawa","year":"2015","unstructured":"Namikawa, T.: Recent advances in near-infrared fluorescence-guided imaging surgery using indocyanine green. Surg Today 45(12), 1467\u20131474 (2015)","journal-title":"Surg Today"},{"issue":"6","key":"7_CR10","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.crad.2011.01.009","volume":"66","author":"M Kontos","year":"2011","unstructured":"Kontos, M., Wilson, R., Fentiman, I.: Digital infrared thermal imaging (DITI) of breast lesions: sensitivity and specificity of detection of primary breast cancers. Clin. Radiol. 66(6), 536\u2013539 (2011)","journal-title":"Clin. Radiol."},{"key":"7_CR11","doi-asserted-by":"publisher","unstructured":"Cardoso, F., et al.: Research needs in breast cancer. Ann. Oncol. (2016). https:\/\/doi.org\/10.1093\/annonc\/mdw571","DOI":"10.1093\/annonc\/mdw571"},{"key":"7_CR12","unstructured":"Breast Cancer: Stages. Cancer.Net. https:\/\/www.cancer.net\/cancer-types\/breast-cancer\/stages"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1007\/s10552-017-0965-0","volume":"28","author":"M Unar-Mungu\u00eda","year":"2017","unstructured":"Unar-Mungu\u00eda, M.: Economic and disease burden of breast cancer associated with suboptimal breastfeeding practices in Mexico. Cancer Causes Control 28, 1381 (2017)","journal-title":"Cancer Causes Control"},{"key":"7_CR14","unstructured":"Lab, V.: A methodology for breast disease computer-aided diagnosis using dynamic thermography. Visual Lab. http:\/\/visual.ic.uff.br\/en\/proeng\/thiagoelias\/"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Szegedy, C.: Going deeper with convolutions, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"7_CR16","unstructured":"scikit-learn.org: sklearn.svm.LinearSVC. sklearn. http:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.svm.LinearSVC.html"},{"issue":"9","key":"7_CR17","doi-asserted-by":"publisher","first-page":"89","DOI":"10.3390\/fi10090089","volume":"10","author":"S Mambou","year":"2018","unstructured":"Mambou, S., Krejcar, O., Kuca, K., Selamat, A.: Novel cross-view human action model recognition based on the powerful view-invariant features technique. Future Internet 10(9), 89 (2018)","journal-title":"Future Internet"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics and Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-17935-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:09:11Z","timestamp":1710335351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-17935-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030179342","9783030179359"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-17935-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"13 April 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWBBIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Bioinformatics and Biomedical Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwbbio2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwbbio.ugr.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}