Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach

dc.contributor.author	Comert, Zafer
dc.contributor.author	Kocamaz, Adnan Fatih
dc.date.accessioned	16/12/21 12:07
dc.date.available	16/12/21 12:07
dc.date.issued	2019
dc.identifier.isbn	978-3-319-91186-1; 978-3-319-91185-4
dc.identifier.issn	2194-5357
dc.identifier.uri	http://dspace.beu.edu.tr:8080/xmlui/handle/20.500.12643/10201
dc.identifier.uri	https://doi.org/10.1007/978-3-319-91186-1_25
dc.description.abstract	Electronic fetal monitoring (EFM) device which is used to record Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals simultaneously is one of the significant tools in terms of the present obstetric clinical applications. In clinical practice, EFM
dc.language.iso	English
dc.publisher	Sprınger Internatıonal Publıshıng Ag
dc.relation.ispartof	7th Computer Science On-Line Conference (CSOC)
dc.source	Software Engıneerıng And Algorıthms In Intellıgent Systems
dc.title	Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
dc.type	Proceedings Paper
dc.identifier.startpage	239
dc.identifier.endpage	248
dc.identifier.doi	10.1007/978-3-319-91186-1_25
dc.identifier.wos	WOS:000445094400025
dc.identifier.volume	763

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