Various Methods for Computing Risk Factors of Down Syndrome in Fetus

Author:

Kumar Sushil,Selvakumar K.

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. Thomas MC, Arjunan SP (2022) Deep learning measurement model to segment the nuchal translucency region for the early identification of down syndrome. Journal homepage. https://doi.org/10.2478/msr-2022-0023. https://content.sciendo.com

2. Nirmala S, Palanisamy V (2009) Measurement of nuchal translucency thickness in first-trimester ultrasound fetal images for detection of chromosomal abnormal- ities. In: 2009 international conference on control automation, communication and energy conservation, INCACEC 2009, vol 3(3), pp 662–668

3. Rafeek T, Gunasundari A (2013) Reliable non-invasive first trimester screening test using image processing and artificial neural network. Int J Res Eng Post Appl 3(3):662–668

4. Anjit TA, Rishidas S (2011) Identification of nasal bone for the early detection of down syndrome using back propagation neural network. In: 2011 Int Conf Commun Signal Process, pp 136–140

5. Sciortino G, Tegolo D (2017) A non-supervised approach to locate and to measure the nuchal translucency by means of wavelet analysis and neural networks. In: Int Conf Inf Commun Autom Technol

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