Using deep‐learning algorithms to classify fetal brain ultrasound images as normal or abnormal

ABSTRACT Objectives To evaluate the feasibility of using deep‐learning algorithms to classify as normal or abnormal sonographic images of the fetal brain obtained in standard axial planes. Methods We included in the study images retrieved from a large hospital database from 10 251 normal and 2529 ab...

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Veröffentlicht in:	Ultrasound in obstetrics & gynecology 2020-10, Vol.56 (4), p.579-587
Hauptverfasser:	Xie, H. N., Wang, N., He, M., Zhang, L. H., Cai, H. M., Xian, J. B., Lin, M. F., Zheng, J., Yang, Y. Z.
Format:	Artikel
Sprache:	eng
Schlagworte:	Abnormalities Accuracy Algorithms Autopsies Autopsy Brain Brain mapping Classification convolutional neural network Data processing Datasets Deep learning Differential diagnosis fetal intracranial structure Fetuses Image classification Image processing Image segmentation Learning algorithms Lesions Localization Mathematical analysis Medical imaging Neonates Neural networks prenatal ultrasound Recall Training Ultrasonic imaging Ultrasound
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