Abstract
In present scenario women’s are suffering from thyroid, diabetes and high blood pressure and therefore early detection and diagnosis of fetal abnormality can save lives and reduce cost of treatment. In this paper we propose an artificial neural network (ANN) based method for the detection of fetal abnormality in 2-D ultrasound images of 14–40 weeks. The accurate values of fetal anatomical structures are found by segmentation techniques and these values transferred to neural model for detection of possible abnormalities from 2D fetal ultrasound images. The ANN model is able to find Intrauterine Growth Retardation (IUGR) and abnormal fetus using head and abdominal circumference.
Keywords
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Rawat, V., Jain, A., Shrimali, V., Rawat, A. (2016). Automatic Detection of Fetal Abnormality Using Head and Abdominal Circumference. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_50
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DOI: https://doi.org/10.1007/978-3-319-45246-3_50
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