{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T14:00:44Z","timestamp":1762092044972,"version":"build-2065373602"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T00:00:00Z","timestamp":1689984000000},"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":["SIViP"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11760-023-02689-7","type":"journal-article","created":{"date-parts":[[2023,7,22]],"date-time":"2023-07-22T10:01:34Z","timestamp":1690020094000},"page":"4543-4550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images"],"prefix":"10.1007","volume":"17","author":[{"given":"Yesim","family":"Eroglu","sequence":"first","affiliation":[]},{"given":"Muhammed","family":"Yildirim","sequence":"additional","affiliation":[]},{"given":"Ahmet","family":"Cinar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,22]]},"reference":[{"issue":"1","key":"2689_CR1","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.pedneo.2014.05.002","volume":"56","author":"BH Su","year":"2015","unstructured":"Su, B.H., Hsieh, W.S., Hsu, C.H., Chang, J.H., Lien, R., Lin, C.H., Taiwan PBF: Neonatal outcomes of extremely preterm infants from Taiwan: comparison with Canada, Japan, and the USA. Pediatr. Neonatol. 56(1), 46\u201352 (2015)","journal-title":"Pediatr. Neonatol."},{"issue":"4","key":"2689_CR2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.ijdevneu.2011.02.012","volume":"29","author":"JJ Volpe","year":"2011","unstructured":"Volpe, J.J., et al.: The developing oligodendrocyte: key cellular target in brain injury in the premature infant. Int. J. Dev. Neurosci. 29(4), 423\u2013440 (2011)","journal-title":"Int. J. Dev. Neurosci."},{"issue":"2","key":"2689_CR3","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.clp.2018.01.015","volume":"45","author":"CM Novak","year":"2018","unstructured":"Novak, C.M., Ozen, M., Burd, I.: Perinatal brain injury: mechanisms, prevention, and outcomes. Clin. Perinatol. 45(2), 357\u2013375 (2018)","journal-title":"Clin. Perinatol."},{"key":"2689_CR4","first-page":"10","volume":"79","author":"A Cerisola","year":"2019","unstructured":"Cerisola, A., Baltar, F., Ferr\u00e1n, C., Turcatti, E.: Mecanismos de lesi\u00f3n cerebral en ni\u00f1os prematuros. MEDICINA (Buenos Aires) 79, 10\u201314 (2019)","journal-title":"MEDICINA (Buenos Aires)"},{"issue":"1","key":"2689_CR5","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.clp.2013.10.003","volume":"41","author":"SH Kwon","year":"2014","unstructured":"Kwon, S.H., et al.: The role of neuroimaging in predicting neurodevelopmental outcomes of preterm neonates. Clin. Perinatol. 41(1), 257\u2013283 (2014)","journal-title":"Clin. Perinatol."},{"key":"2689_CR6","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.nicl.2017.08.015","volume":"16","author":"M Hinojosa-Rodr\u00edguez","year":"2017","unstructured":"Hinojosa-Rodr\u00edguez, M., Harmony, T., Carrillo-Prado, C., Van Horn, J.D., Irimia, A., Torgerson, C., Jacokes, Z.: Clinical neuroimaging in the preterm infant: diagnosis and prognosis. NeuroImage Clin 16, 355\u2013368 (2017)","journal-title":"NeuroImage Clin"},{"key":"2689_CR7","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/B978-0-444-64029-1.00007-2","volume":"162","author":"J Schneider","year":"2019","unstructured":"Schneider, J., Miller, S.P.: Preterm brain injury: White matter injury. Handb. Clin. Neurol. 162, 155\u2013172 (2019)","journal-title":"Handb. Clin. Neurol."},{"key":"2689_CR8","doi-asserted-by":"publisher","first-page":"107473","DOI":"10.1016\/j.knosys.2021.107473","volume":"232","author":"F Murat","year":"2021","unstructured":"Murat, F., et al.: Exploring deep features and ECG attributes to detect cardiac rhythm classes. Knowl.-Based Syst. 232, 107473 (2021)","journal-title":"Knowl.-Based Syst."},{"issue":"6","key":"2689_CR9","doi-asserted-by":"publisher","first-page":"e12474","DOI":"10.1111\/exsy.12474","volume":"38","author":"U Raghavendra","year":"2021","unstructured":"Raghavendra, U., et al.: 2DSM vs FFDM: A computeraided diagnosis based comparative study for the early detection of breast cancer. Expert. Syst. 38(6), e12474 (2021)","journal-title":"Expert. Syst."},{"issue":"8","key":"2689_CR10","doi-asserted-by":"publisher","first-page":"1446","DOI":"10.3390\/diagnostics11081446","volume":"11","author":"O Faust","year":"2021","unstructured":"Faust, O., et al.: Automated arrhythmia detection based on RR intervals. Diagnostics 11(8), 1446 (2021)","journal-title":"Diagnostics"},{"key":"2689_CR11","doi-asserted-by":"publisher","first-page":"102733","DOI":"10.1016\/j.bspc.2021.102733","volume":"68","author":"A Gudigar","year":"2021","unstructured":"Gudigar, A., et al.: Automated detection of chronic kidney disease using image fusion and graph embedding techniques with ultrasound images. Biomed. Signal Process. Control 68, 102733 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"2689_CR12","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"2689_CR13","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.clinimag.2017.11.007","volume":"49","author":"RC Mayo","year":"2018","unstructured":"Mayo, R.C., Leung, J.: Artificial intelligence and deep learning\u2013Radiology\u2019s next frontier? Clin. Imaging 49, 87\u201388 (2018)","journal-title":"Clin. Imaging"},{"key":"2689_CR14","unstructured":"Tan, M., & Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In\u00a0International conference on machine learning\u00a0(pp. 6105-6114). PMLR. (2019)"},{"key":"2689_CR15","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"2689_CR16","unstructured":"Howard, A.G., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, (2017)"},{"key":"2689_CR17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2689_CR18","doi-asserted-by":"crossref","unstructured":"Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2689_CR19","doi-asserted-by":"crossref","unstructured":"He, K., et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"2689_CR20","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/ima.22623","volume":"32","author":"M Yildirim","year":"2022","unstructured":"Yildirim, M., Cinar, A.: Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET. Int J Imag Syst Technol 32(1), 155\u2013162 (2022)","journal-title":"Int J Imag Syst Technol"},{"key":"2689_CR21","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2011.10.021","volume":"83","author":"W Yang","year":"2012","unstructured":"Yang, W., Wang, K., Zuo, W.: Fast neighborhood component analysis. Neurocomputing 83, 31\u201337 (2012)","journal-title":"Neurocomputing"},{"key":"2689_CR22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.eswa.2018.06.031","volume":"113","author":"S Raghu","year":"2018","unstructured":"Raghu, S., Sriraam, N.: Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms. Expert Syst. Appl. 113, 18\u201332 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"2689_CR23","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/ima.22806","volume":"33","author":"O Ozaltin","year":"2023","unstructured":"Ozaltin, O., et al.: Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm. Int. J. Imaging Syst. Technol. 33(1), 69\u201391 (2023)","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"20","key":"2689_CR24","doi-asserted-by":"publisher","first-page":"14837","DOI":"10.1007\/s00521-023-08491-3","volume":"35","author":"S Dogan","year":"2023","unstructured":"Dogan, S., et al.: A new hand-modeled learning framework for driving fatigue detection using EEG signals. Neural Comput. Appl. 35(20), 14837\u201314854 (2023)","journal-title":"Neural Comput. Appl."},{"issue":"3","key":"2689_CR25","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.ejrad.2007.04.016","volume":"65","author":"L Liauw","year":"2008","unstructured":"Liauw, L., et al.: Differentiation between peritrigonal terminal zones and hypoxic-ischemic white matter injury on MRI. Eur. J. Radiol. 65(3), 395\u2013401 (2008)","journal-title":"Eur. J. Radiol."},{"key":"2689_CR26","first-page":"694","volume":"28","author":"E Cengil","year":"2021","unstructured":"Cengil, E., \u00c7INAR A, YILDIRIM M: Hybrid convolutional neural network architectures for skin cancer classification. Avrupa Bilim Ve Teknoloji Dergisi 28, 694\u2013701 (2021)","journal-title":"Avrupa Bilim Ve Teknoloji Dergisi"},{"key":"2689_CR27","doi-asserted-by":"crossref","unstructured":"Coriddi, M., et al.: Accuracy, Sensitivity, and Specificity of the LLIS and ULL27 in Detecting Breast Cancer-Related Lymphedema. Annals of surgical oncology pp 1-8 (2021)","DOI":"10.1245\/s10434-021-10469-1"},{"key":"2689_CR28","doi-asserted-by":"crossref","unstructured":"Anderson PJ, Cheong JL, Thompson DK.: The predictive validity of neonatal MRI for neurodevelopmental outcome in very preterm children. In: Seminars in perinatology\u00a0(Vol 39, No 2, pp 147\u2013158). WB Saunders (2015)","DOI":"10.1053\/j.semperi.2015.01.008"},{"issue":"2","key":"2689_CR29","first-page":"57","volume":"65","author":"GJ Romero-Guzman","year":"2017","unstructured":"Romero-Guzman, G.J., Lopez-Munoz, F.: Prevalence and risk factors for periventricular leukomalacia in preterm infants. Syst Rev Revista de Neurol 65(2), 57\u201362 (2017)","journal-title":"Syst Rev Revista de Neurol"},{"key":"2689_CR30","unstructured":"Zhao, W. T., & Yu, H. M.: Research progress on periventricular white matter damage pathogenesis in preterm infants.\u00a0Zhongguo Dang dai er ke za zhi= Chinese Journal of Contemporary Pediatrics,\u00a015(5), 396-following (2013)"},{"issue":"8","key":"2689_CR31","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/s00431-004-1477-y","volume":"163","author":"I Blumenthal","year":"2004","unstructured":"Blumenthal, I.: Periventricular leucomalacia: a review. Eur. J. Pediatr. 163(8), 435\u2013442 (2004)","journal-title":"Eur. J. Pediatr."},{"issue":"1","key":"2689_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4103\/1817-1745.205646","volume":"12","author":"S Bano","year":"2017","unstructured":"Bano, S., Chaudhary, V., Garga, U.C.: Neonatal hypoxic-ischemic encephalopathy: A radiological review. J. Pediatr. Neurosci. 12(1), 1 (2017)","journal-title":"J. Pediatr. Neurosci."},{"issue":"Suppl 1","key":"2689_CR33","doi-asserted-by":"publisher","first-page":"S125","DOI":"10.21037\/tp.2020.01.01","volume":"9","author":"DR Patel","year":"2020","unstructured":"Patel, D.R., et al.: Cerebral palsy in children: a clinical overview. Trans Pediatr 9(Suppl 1), S125 (2020)","journal-title":"Trans Pediatr"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02689-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02689-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02689-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T18:18:46Z","timestamp":1694542726000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02689-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,22]]},"references-count":33,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["2689"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02689-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2023,7,22]]},"assertion":[{"value":"28 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The dataset used in the study was obtained from Firat University, Department of Radiology. Approval was obtained from the ethics committee of the university for the study (session date: 23.09.2021; number of sessions: 2021\/10\u201301).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}