{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:18:07Z","timestamp":1743045487090,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031091346"},{"type":"electronic","value":"9783031091353"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-09135-3_20","type":"book-chapter","created":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T19:02:22Z","timestamp":1655578942000},"page":"234-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artifact Detection on\u00a0X-ray of\u00a0Lung with\u00a0COVID-19 Symptoms"],"prefix":"10.1007","author":[{"given":"Alicja","family":"Moskal","sequence":"first","affiliation":[]},{"given":"Magdalena","family":"Jasionowska-Skop","sequence":"additional","affiliation":[]},{"given":"Grzegorz","family":"Ostrek","sequence":"additional","affiliation":[]},{"given":"Artur","family":"Przelaskowski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,19]]},"reference":[{"key":"20_CR1","unstructured":"World Health Organization: Global research on coronavirus disease (COVID-19). https:\/\/www.who.int\/emergencies\/diseases\/novel-coronavirus-2019\/global-research-on-novel-coronavirus-2019-ncov"},{"issue":"3","key":"20_CR2","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1148\/radiol.2021204522","volume":"299","author":"JP Kanne","year":"2021","unstructured":"Kanne, J.P., et al.: COVID-19 imaging: what we know now and what remains unknown. Radiology 299(3), 262\u2013279 (2021). https:\/\/doi.org\/10.1148\/radiol.2021204522","journal-title":"Radiology"},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Heidari, M., Mirniaharikandehei, S., Zargari, A., et al.: Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray with preprocessing algorithms. Int. J. Med. Inform. 144 (2020). 104284, ISSN 1386\u20135056, https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104284","DOI":"10.1016\/j.ijmedinf.2020.104284"},{"key":"20_CR4","unstructured":"Uras, I., Yavuz, O.Y., Kose, K.C., Atalar, H., Uras, N., Karadag, A.: Radiographic artifact mimicking epiphysis of the femoral head in a seven-month-old girl. J. Natl. Med. Assoc. 98(7), 1181\u20131182 (2006). PMID: 16895292; PMCID: PMC2569463"},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Mestayer, R.G., Attaway, K.C., Polchow ,T.N., Brogdon, B.G.: Snooping around the adolescent pelvis: good grief, it\u2019s the brief! AJR Am. J. Roentgenol. 186(2):587\u2013588. PMID: 16423982. https:\/\/doi.org\/10.2214\/AJR.05.0816","DOI":"10.2214\/AJR.05.0816"},{"key":"20_CR6","doi-asserted-by":"publisher","unstructured":"Hogeweg, L., et al.: Foreign object detection and removal to improve automated analysis of chest radiographs. Med Phys. 40(7), 071901 (2013). PMID: 23822438. https:\/\/doi.org\/10.1118\/1.4805104","DOI":"10.1118\/1.4805104"},{"issue":"3","key":"20_CR7","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/s42979-021-00496-w","volume":"2","author":"A Sarkar","year":"2021","unstructured":"Sarkar, A., et al.: Identification of of COVID-19 from chest X-rays using deep learning: comparing COGNEX VisionPro deep learning 1.0 software with open source convolutional neural networks. SN Comput. Sci. 2(3), 130 (2021)","journal-title":"SN Comput. Sci."},{"key":"20_CR8","doi-asserted-by":"publisher","unstructured":"Murphy, A.: Clothing artifact. Case study, Radiopaedia.org. https:\/\/doi.org\/10.53347\/rID-59812. Accessed 18 Jan 2022","DOI":"10.53347\/rID-59812"},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Subramaniam, U., Monica Subashini, M., Almakhles, D., Karthick, A., Manoharan, S.: An Expert system for covid-19 infection tracking in lungs using image processing and deep learning techniques. BioMed Res. Int. 2021 (2021). Article ID 1896762, 17 pages. https:\/\/doi.org\/10.1155\/2021\/1896762","DOI":"10.1155\/2021\/1896762"},{"key":"20_CR10","doi-asserted-by":"publisher","unstructured":"Heidari, M., et al.: Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray with preprocessing algorithms. Int. J. Med. Inform. 144, 104284 (2020). ISSN 1386\u20135056, https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104284","DOI":"10.1016\/j.ijmedinf.2020.104284"},{"key":"20_CR11","doi-asserted-by":"publisher","unstructured":"Xue, Z., et al.: Foreign object detection in chest X-rays. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, pp. 956\u2013961 (2015). https:\/\/doi.org\/10.1109\/BIBM.2015.7359812","DOI":"10.1109\/BIBM.2015.7359812"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Przelaskowski, A., Jasionowska, M., Ostrek, G.: \u2019Semantic segmentation of abnormal lung areas on chest X-rays to detect COVID-19\u2019. Submitted to ITIB 2022","DOI":"10.1007\/978-3-031-09135-3_21"},{"key":"20_CR13","unstructured":"Nguyen, H.Q., et al.: VinDr-CXR: An open dataset of chest X-rays with radiologist\u2019s annotations (2020)"},{"issue":"2","key":"20_CR14","doi-asserted-by":"publisher","first-page":"E72","DOI":"10.1148\/radiol.2020201160","volume":"296","author":"H Wong","year":"2020","unstructured":"Wong, H., Lam, H., Fong, A.T., Leung, S., Chin, T.Y., Lo, C., Lui, M.S., Lee, J., Chiu, K.H., Chung, T.H., Lee, E., Wan, E., Hung, I., Lam, T., Kuo, M., Ng, M.Y.: Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology 296(2), E72\u2013E78 (2020)","journal-title":"Radiology"},{"issue":"3","key":"20_CR15","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1145\/357994.358023","volume":"27","author":"TY Zhang","year":"1984","unstructured":"Zhang, T.Y., Suen, C.Y.: A fast parallel algorithm for thinning digital patterns. Commun. ACM 27(3), 236\u2013239 (1984)","journal-title":"Commun. ACM"},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/s12553-021-00520-2","volume":"11","author":"JD Lopez-Cabrera","year":"2021","unstructured":"Lopez-Cabrera, J.D., Orozco-Morales, R., et al.: Current limitations to identify COVID-19 using artifcial intelligence with chest X-ray imaging. Health Technol. 11, 411\u2013424 (2021)","journal-title":"Health Technol."},{"key":"20_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.inffus.2021.04.008","volume":"76","author":"G Maguolo","year":"2021","unstructured":"Maguolo, G., Nanni, L.: A critic evaluation of methods for COVID-19 automatic detection from X-ray. Inf. Fus. 76, 1\u20137 (2021)","journal-title":"Inf. Fus."}],"container-title":["Advances in Intelligent Systems and Computing","Information Technology in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09135-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T19:45:57Z","timestamp":1675885557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09135-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031091346","9783031091353"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09135-3_20","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ITIB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Technologies in Biomedicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kamie\u0144 \u015al\u0105ski","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","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":"20 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"itib2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/itib.polsl.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}