Abstract
Fetal ultrasound scanning helps to measure fetal viability, gestational age, biometric, and identification of any occurrences of congenital abnormalities. The biometrics used to measure fetal growth include biparietal diameter (BPD), abdominal circumference (AC), head circumference (HC), and femur length (FL). Prenatal ultrasound scans must have anatomical measurements in order to track the fetus’s growth and development, which heavily depends on getting a standard plane (SP). Standard plane identification from an ultrasound image is a difficult task. The three different classifications of standard planes of fetal US brain are trans-cerebellum, trans-thalamic, and trans-ventricular planes. A model is proposed in this paper for the fetal brain standard planes classification into four classes such as trans-cerebellum, trans-thalamic, trans-ventricular, and others using deep learning algorithms such as ResNet50, VGG16 and VGG19. Performance of the model is evaluated using parameters like accuracy, precision, recall, and F1 Score.
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Shanavas, J., Kanjana, G. (2023). Standard Plane Classification of Fetal Brain Ultrasound Images. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_41
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DOI: https://doi.org/10.1007/978-981-99-4626-6_41
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