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Analyzing the Effects of Alternative Decisions in a Multiagent System with Stigmergy-Based Interactions Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection

Authors : Vidhi Rawat, Alok Jain, Vibhakar Shrimali, Sammer Raghuvanshi

Published in: Transactions on Computational Collective Intelligence XXX

Publisher: Springer International Publishing

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Abstract

Ultrasound imaging is one of the safest and most effective method generally used for the diagnosis of fetal growth. The precise assessment of fetal growth at the time of pregnancy is tough task but ultrasound imaging have improved this vital aspect of Obstetrics and Gynecology. In this paper performance of different learning algorithms of Feed forward neural network based on back-propagation algorithm are analyzed and compared. Basically detection of fetal abnormality using neural network is a hybrid method, in which biometric parameters are extracted and measured from segmentation techniques. Then extracted value of biometric parameters are applied on neural network for detect the fetus status. The artificial neural network (ANN) model is applied for the better diagnosis and effective classification purpose. ANN model are design to discriminate normal and abnormal fetus based on the 2-D US images. In this paper, 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. Performance 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 modeling will help radiologist to take appropriate decision in the boundary line cases.

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Literature
1.
2.
Hearn-Stebbins, B.: Normal fetal growth assessment: a review of the literature and current practice. J. Diagn. Med. Sonog. 11(4), 176–187 (1995)CrossRef
3.
Honarvar, M., Allahyari, M., Dehbashi, S.: Assessment of gestational age based on ultrasonic femur length after the first trimester: a simple mathematical correlation between gestational age (GA) and femur length (FL). Int. J. Gynecol. 70(3), 335–340 (2000)CrossRef
4.
Hanna, E.C.W., Youssef, A.: Automated measurements in obstetric ultrasound images. In: Proceedings of the 1997 International Conference on Image Processing. Part 3, Santa Barbara, CA, USA, pp. 504–507. IEEE Comp Soc, Los Alamitos (1997)
5.
Thomas, J.S., Peters II, R.A., Jeanty, P.: Automatic segmentation of ultrasound images using morphological operators. IEEE Trans. Med. Imaging 10(2), 180–186 (1991)CrossRef
6.
Shrimali, V., Anand, R.S., Kumar, V.: Improved segmentation of ultrasound images for fetal biometry using morphological operators. In: 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, 2–6 September 2009
7.
Rawat, V., Jain, A., Shrimali, V.: Investigation and assessment of US images for fetal biometry using morphological operator. In: 3rd International Conference on Artificial Intelligence, IICAI 2011, Tumkur (2011)
8.
Rueda, S., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. (2013)
9.
Jinhua, Yu., Wang, Y., Chen, P.: Fetal ultrasound image segmentation system and its use in fetal weight estimation. Ultrasound Med. Biol. 46, 1227–1237 (2008)
10.
Pathak, S.D., Chalana, V., Kim, Y.: Interactive automatic fetal head measurements from ultrasound images using multimedia technology. Ultrasound Med. Biol. 23(5), 665–673 (1997)CrossRef
11.
Nithya, J., Madheswaran, M.: Detection of intrauterine growth retardation using fetal abdominal circumference. In: International Conference on Computer Technology and Development (2009)
12.
Jinhua, Yu., Wang, Y., Chen, P., Shen, Y.: Fetal abdominal contour extraction and measurement in ultrasound images. Ultrasound Med. Biol. 34(2), 169–182 (2008)CrossRef
13.
Ciurte, A., et al.: Ultrasound image segmentation of the fetal abdomen: a semi-supervised patch-based approach. In: Proceedings of Challenge US: Biometric Measurements from Fetal Ultrasound Images, ISBI 2012, pp. 13–15 (2012)
14.
Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Trans. Med. Imaging 27(9), 1342–1355 (2008)CrossRef
15.
Carneiro, G., Georgescu, B., Good, S.: Knowledge-based automated fetal biometrics using syngo Auto OB measurements. Siemens Medical Solutions (2008)
16.
Sarris, C., et al.: Intra- and inter-observer variability in fetal ultrasound measurements. Ultrasound Obstet. Gynecol. 39(3), 266–273 (2012)CrossRef
17.
Gurgen, F., Onal, E., Varol, F.G.: IUGR detection by ultrasonography examinations using neural networks. IEEE Eng. Med. Biol. Mag. 16, 55–58 (1997)CrossRef
18.
Bagi, K.S., Shreedhara, K.S.: Biometric measurement and classification of IUGR using neural networks. In: IEEE Conference on Computing and Informatics (2014)
19.
Yu, L., Guo, Y., Wang, Y., Yu, J., Chen, P.: Segmentation of fetal left ventricle echocardiographic sequence based on dynamic convolutional neural networks. IEEE Trans. Biomed. Imaging (2016). https://​doi.​org/​10.​1109/​tbme
20.
Khashman, A., Curtis, K.M.: Automatic edge detection of foetal head and abdominal circumferences using neural network arbitration. In: ISIE 1997 - Guimarses, Portugal (1997)
21.
Khashman, A., Curtis, K.: Neural networks arbitration for automatic edge detection of 3-dimensional objects. In: Proceeding of 3rd IEEE Interactional Conference on Electronics, Circuits and Systems, ICECS 1996, pp. 49–52 (1996)
22.
Rahmatullah, B., Papageorghiou, A.T., Noble, J.A.: Image analysis using machine learning anatomical landmarks detection in fetal ultrasound images. In: IEEE Conference on Computer Application, pp. 354–355 (2012)
23.
Bibicu, D., Moraru, L.: Cardiac cycle phase estimation in 2-D Echocardiographic using an artificial neural network. IEEE Trans. Biomed. Eng. 60(5), 1273–1279 (2013)CrossRef
24.
25.
Perez, R.R., Marques, A., Mohammadi, F.: The application of supervised learning through feed –forward neural networks for ECG signal classification. In: IEEE Conference on Electrical and Computer Engineering (2016)
26.
27.
Gonzales, R., Wood, R., Eddins, S.: Digital Image Processing Using MATLAB. Gates Mark Publishing, Houston (2009)
28.
Rawat, V., Jain, A., Shrimali, V.: Investigation and assessment of disorder of ultrasound B-mode images. Int. J. Comput. Sci. Inf. Secur. 7(2) (2010)
29.
Rawat, V., Jain, A., Shrimali, V.: Automatic assessment of fetal biometric parameter using GVF snakes. J. Biomed. Eng. Technol. Indersci. 12(4), 221–233 (2013)
30.
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 321–331 (1988)CrossRef
31.
32.
Cohen, M.: Neural Networks and artificial intelligence for biomedical engineering. In: IEEE Press Series on Biomedical Engineering (2000)
33.
34.
Levenberg, K.: A method for the solution of certain nonlinear problems in least squares. Q. Appl. Math. 2(2), 164–168 (1944)MathSciNetCrossRef
35.
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRef
36.
Hagan, M.T., Menhaj, M.: Training feed forward network with the Marquardt algorithm. IEEE Trans. Neural Netw. 5(6), 989–993 (1999)CrossRef
37.
Kisi, O., Uncuoghlu, E.: Comparison of three back propagation training algorithm for two case studies. Indian J. Eng. Mater. Sci. 12, 434–444 (2005)
38.
Wilamowski, B.M.: Neural network architectures and learning algorithms. IEEE Ind. Electron. Mag. 3(4), 56–63 (2009)CrossRef
39.
Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning neural networks. 6, 525–553 (1993)
40.
Yue, Z., Songzheng, Z., Tianshi, L.: Bayesian regularization BP neural network model for predicting oil–gas drilling cost. Int. Conf. Bus. Manag. Electron. Inf. (BMEI) 2, 483–487 (2011)
41.
Winham, S., Slater, A.J.: A comparison of internal validation techniques for multifactor dimensionality reduction. BMC Bioinf. 1–16 (2010)
42.
Onyia, T.: Analysis of Case Study: Fetal Abnormality. Grand Canyon University: PHI 413V, 13 November 2016
43.
Ferreira da Costa, L.L., Hardy, E., Duarte Osis, M.J., Faúndes, A.: Termination of pregnancy for fetal abnormality incompatible with life: women’s experiences in Brazil. Report Health Matters 13(26), 139–146 (2005)
44.
Pregnancy for Fetal Abnormality in England, Scotland and Wales, Report of a Working Party, May 2010
45.
Baumgartner, C.F., et al.: SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017)CrossRef
46.
Baumgartner, C.F., Kamnitsas, K., Matthew, J., Smith, S., Kainz, B., Rueckert, D.: Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 203–211. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46723-8_​24CrossRef
47.
48.
Supriyanto, E., Wee, L.K., Min, T.Y.: Ultrasonic marker pattern recognition and measurement using artificial neural network. In: SIP 2010 Proceedings of the 9th WSEAS International Conference on Signal Processing, Catania, Italy, pp. 35–40, 29–31 May 2010
49.
50.
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)MATH
51.
Kröse, B., Smagt, P.V.D.: An introduction to Neural Networks. The University of Amsterdam, Amsterdam (1996)
52.
Christodoulou, C.G., Georgiopoulos, M.: Application of Neural Networks in Electromagnetics. Artech House, Norwood (2001)
53.
Sharma, M., Achuth, P., Pachoria, R.B., Gadreb, V.M.: A parametrization technique to design joint time-frequency optimized discrete-time biorthogonal wavelet bases. Sig. Process. 135, 107–120 (2017)CrossRef
54.
Sharma, M., Dhere, A., Pachori, R.B., Gadre, V.M.: Optimal duration-bandwidth localized antisymmetric bi-orthogonal wavelet filters. Sig. Process. 134, 87–99 (2017)CrossRef
55.
Sharma, M., Bhati, D., Pillai, S., Pachori, R.B., Gadre, V.M.: Design of time frequency localized filter banks: transforming non convex problem into convex via semidefined relaxation techniques. Circuit Syst. Signal Process. 1–18 (2016)
56.
Metadata
Title
Performance Analysis of Different Learning Algorithms of Feed Forward Neural Network Regarding Fetal Abnormality Detection
Authors
Vidhi Rawat
Alok Jain
Vibhakar Shrimali
Sammer Raghuvanshi
Copyright Year
2018
Book
Transactions on Computational Collective Intelligence XXX

Print ISBN: 978-3-319-99809-1

Electronic ISBN: 978-3-319-99810-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99810-7_6

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