Abstract
This chapter proposes an approach of neural modeling for the diagnosis of fetus abnormality using ultrasound (US) images. The proposed method is a hybrid approach of image processing methods and artificial neural network as a classifier to extract fetus abnormality. In the first step, 50 US images in the DICOM format are taken from the radiologist, preprocess these images by segmenting the fetal biometric parameters using the morphological operator and the gradient vector flow algorithm. The segmented parameters were labeled as normal and abnormal fetal parameters. The extracted parameters were used to train a feed-forward back-propagation neural network. Then, in the next step, 500 US images are taken to process the neural model. The feed-forward back-propagation neural network using Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms are analyzed and used for diagnosis and classification of fetal growth. Performances of these methods are compared and evaluated based on desired output and mean square error. Results found from the Bayesian-based neural networks are in closed confirmation with the real time results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
C. W. Hanna and A. B. M. Youssef, “Automated measurements in obstetric ultrasound images”, Proceedings of IEEE International Conference on Image Processing, Santa Barbara, CA, Vol. 3, pp. 504–507, 1997.
J. J. Cronan, “Ultrasound: Is there a future in diagnostic imaging?”, Journal of the American college of Radiology, Vol. 3, Issue 09, pp. 645–646, 2006.
M. W. Miller, A. A. Brayman, J. S. Abramowicz, “Obstetric Ultrasonography: A biophysical consideration of patient safety the “rules” have changed”, American Journal of Obstetric Gynecology Vol. 179, Issue 01, pp. 241–254, 1998.
R. Sanders and A. James, “The Principles and Practice of Ultrasonography in Obstetrics and Gynecology”, 3rdedition, Appleton-Century-Crofts, Connecticut, Ch. 9 & 10, 2011.
S. L. Bridal, J.M. Correas, A. Saied, and P. Laugier, “Milestones on the road to higher resolution, quantitative, and functional ultrasonic imaging”, Proceeding IEEE, Vol. 91, No. 10, pp. 1543–1561, Oct. 2003.
D. C. L.L Ferreira, E. Hardy, M. J.O. Duarte, A. Faúndes, “Termination of Pregnancy for Fetal Abnormality Incompatible with Life: Women’s Experiences in Brazil” Report Health Matters. Vol. 13, No 26, pp.139–46, Nov. 2005.
Priddy K.L., Keller P.E., “Introduction in Artificial Neural Networks: An Introduction”, Bellingham Wash., St. Bellingham USA, pp.1–12, 2005.
**nxing Pan, B. Lee and Chunrong Zhang, “A comparison of neural network backpropagation algorithms for electricity load forecasting”, 2013 IEEE International Workshop on Intelligent Energy Systems (IWIES), pp. 22–27, Vienna, 2013.
S. Haykin, “Neural Networks: A Comprehensive Foundation”, 2nd. Prentice-Hall; Englewood Cliffs, NJ: 1999.
B. Kröse, P. V. D. Smagt, “An introduction to Neural Networks”, The University of Amsterdam; Amsterdam: 1996.
C.G. Christodoulou, M. Georgiopoulos, “Application of Neural Networks in Electromagnetics”, Artech House; MA, USA: 2001.
Levenberg Keneth “A Method For the Solution Of Certain Nonlinear Problems In Least Squares”, Quarterly of Applied Mathematics, Brown University, Vol. 2, pp. 164–168, 1944
Marquardt D., “An Algorithm for Least-Squares Estimation of Nonlinear Parameters”, SIAM Journal on Applied mathematics, Vol.11, No.2, pp.431–441, June 1963.
Hagan M.T. and M. Menhaj, “Training Feed Forward Network with the Marquardt Algorithm”, IEEE Transactions on Neural Network, Vol. 5, No.6, pp. 989–993, 1999.
Martin Fodslette Møller, “A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning Neural Networks”, Neural Networks, Vol. 6, Issue 4, pp. 525–553, 1993.
Zhao Yue, Zhao Songzheng and Liu Tianshi, “Bayesian regularization BP Neural Network model for predicting oil-gas drilling cost”, 2011 International Conference on Business Management and Electronic Information, pp. 483–487, Guangzhou, 2011.
Vidhi Rawat, Alok Jain, Vibhakar Shrimali, Abhishek Rawat, “Automatic Detection of Fetal Abnormality using head and abdominal circumference”, ICCCI 2016, Part II, LNAI, Vol. 9876, pp. 525–534, 2016. (https://doi.org/10.1007/978-3-319-45246-3_50).
Ashwaq Qasem, Siti Norul Huda Sheikh Abdullah, Shahnorbanun Sahran, Tengku Siti Meriam Tengku Wook, Rizuana Iqbal Hussain, Norlia Abdullah, Fuad Ismail, “Breast Cancer Mass Localization Based On Machine Learning”, IEEE 10th International Colloquium on Signal Processing and its Applications, pp. 31–36, Kuala Lumpur, 2014.
Vidhi Rawat, Alok Jain, Vibhakar Shrimali, “Investigation and Assessment of Disorder of Ultrasound B-mode Images”, International Journal of Computer Science and Information Security, Vol.7, No.2, February 2010.
Vidhi Rawat, Alok Jain, Vibhakar Shrimali “Analysis and Assessment of Ultrasound Images for Fetal Biometry Using Morphological Operators”, 5th Indian International Conference on Artificial Intelligence (IICAI-11), pp. 1271–79, Tumkur, India, Dec. 14–16, 2011. (ISBN: 978-0-9727412-8-6).
Vidhi Rawat, Alok Jain, Vibhakar Shrimali, “Analysis of Ultrasound Images for Assessment of Gestational Sac using Gradient Vector Force”, International Journal of Biomedical Engineering and Technology, Inderscience, Vol. 12, No 4, pp. 221–233, 2013. (https://doi.org/10.1504/IJBET.2013.057650).
Vidhi Rawat, Alok Jain, V. Shrimali and Sammer Raghuvansi, “Neural Modeling of Fetal Biometric Parameters for Detection of Fetal Abnormality”, IETE Journal of Research, pp. 1–13, Jan 2019. (https://doi.org/10.1080/03772063.2019.1565948)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rawat, V., Shrimali, V., Jain, A., Rawat, A. (2023). Detection of Fetal Abnormality Using ANN Techniques. In: Paunwala, C., et al. Biomedical Signal and Image Processing with Artificial Intelligence. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-15816-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-15816-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15815-5
Online ISBN: 978-3-031-15816-2
eBook Packages: EngineeringEngineering (R0)