Volume 5, Issue 2, December 2019, Page: 45-50
Medical Images Classification and Diagnostics Using Fuzzy Neural Networks
Yuriy Zaychenko, Institute for Applied System Analysis, Igor Sikorsky Kiev Polytechnic Institute, Kiev, Ukraine
Aghaei Agh Ghamish Ovi Nafas, Department of Applied Mathematics, Igor Sikorsky Kiev Polytechnic Institute, Kiev, Ukraine
Received: Jul. 23, 2019;       Accepted: Aug. 19, 2019;       Published: Sep. 9, 2019
DOI: 10.11648/j.ajnna.20190502.11      View  50      Downloads  18
Abstract
The problem of medical images of cervix epithelium classification for express diagnostics is considered…The following states of cervix epithelium are to be recognized and classified: normal state - columnar epithelium; squamous epithelium (normal state); metaplasia-benign changes of cervix uterus epithelium; CIN1-displasia of light degree, CIN 2-displasia of middle degree, CIN 3-displasia of high degree- intra-epithelium cancer: For its solution the application of fuzzy neural network (FNN NEFClass M) is suggested. The application of FNN is grounded by its following properties: it may work with fuzzy and qualitative information; it has accelerated convergence as compared with crisp classification methods; it enables to attain better classification accuracy than conventional classifiers. The structure of FNN NEFClass and its model description are presented. Training algorithm stochastic gradient descent for membership functions of fuzzy sets is considered and implemented. Data set of medical images of cervix epithelium which was obtained by special device colposcope is described and some images are presented. The experimental investigations of FNN NEFClass application for medical images recognition on real data are carried out, the results are presented. The comparison with NN Back Propagation, RBF NN and cascade RBF NN was made and estimation of efficiency of the suggested approach was performed. The problem of reduction of features number in classification tasks using principal component method (PCM) method is considered and implemented.
Keywords
Medical Images Classification, Medical Diagnostics, FNN NEFClass, Training, Cascade RBFNN, Features Selection, PCM
To cite this article
Yuriy Zaychenko, Aghaei Agh Ghamish Ovi Nafas, Medical Images Classification and Diagnostics Using Fuzzy Neural Networks, American Journal of Neural Networks and Applications. Vol. 5, No. 2, 2019, pp. 45-50. doi: 10.11648/j.ajnna.20190502.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features,” in Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, vol. 61. IEEE, May 2008, pp. 496-499.
[2]
Y. Le Cun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436-444, 2015.
[3]
Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in NeuralInformation Processing Systems 25, 2012, pp. 1097-1105.
[4]
Jianpeng Zhang, Yong Xia, Qi Wu, Yutong Xie. Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning, Xiv:1706.09092v1 [cs. CV] 28 Jun 2017.
[5]
Nilanjan Dey. Classification Technologies Techniques for Medical ImageAnalysis and Computer-aided Diagnosis, Volume 14, 1st Edition.-Academic Press, 201.-218 p.
[6]
Yin, Xiao-Xia, Hadjiloucas, Sillas, Zhang, Yanchun. Pattern Classification of Medical Images: Computer Aided Diagnosis Springer International. Springer International Publishing. 2017.-218 p. DOI. 10.1007/978-3-319-57027-3.Hardcover ISBN.978-3-319-57026-6.
[7]
K. Malyshevska. The analysis of neural networks’ performance for medical image classification/K. Malyshevska//International Journal "Information Content and Processing", Volume 1, Number 2, 2014.-С. 194-199.
[8]
K. Malyshevska. Analysis of neural networks application for diagnostics of uterus cancer using multispectral images. System research and information technologies.-2010-№2-pp. 64-71. (rus)
[9]
Detlef Nauck and Rudolf Kruse. Generating classification rules with the neuro-fuzzy system NEFCLASS. In Proc. Biennial Conf. of the Norght American Fuzzy Information Processing Society (NAFIPS’96), Berkeley, 1996.
[10]
Detlef Nauck and Rudolf Kruse. New learning strategies for NEFCLASS. In Proc. Seventh International Fuzzy Systems Association World Congress IFSA’97, Vol. IV, pp. 50-55, Academia Prague, 1997.
[11]
Zaychenko Yu. P., Sevaee Fatma, Matsak A. V. Fuzzy neural networks for economic data classification//Vestnik of National Technical University of Ukraine “KPI”, section “Informatic, control and c omputer engineering. Vol. 42.-2004.-pp. 121-133. (rus).
[12]
Zaychenko Yu. P., Petrosyuk I. M., Jaroshenko M. S. The investigations of fuzzy neural networks in the problems of electro-optical images recognition//System research and information technologies.-2009.-№4.-pp. 61-76. (rus).
[13]
M. Zgurovsky, Yu. Zaychenko. The Fundamentals of Computational Intelligence: System Approach. Springer International Publishing AG, Switzerland.-2016-308p.
[14]
N. Jindal. Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks./N Jindal, V Kumar//International Journal of Advanced Research in Computer Science and Software Engineering-2013-vol 3 pp. 864-872.
Browse journals by subject