Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

General Review Article

Meta-heuristic Techniques to Train Artificial Neural Networks for Medical Image Classification: A Review

Author(s): Priyanka* and Dharmender Kumar

Volume 15, Issue 4, 2022

Published on: 15 September, 2020

Article ID: e220322185915 Pages: 18

DOI: 10.2174/2666255813999200915141534

Price: $65

Abstract

Medical imaging has been utilized in various forms in clinical applications for better diagnosis and treatment of diseases. These imaging technologies help in recognizing body's ailing region easily. In addition, it causes no pain to the patient as the interior part of the body can be examined without difficulty. Nowadays, various image processing techniques such as segmentation, registration, classification, restoration, contrast enhancement and many more exist to enhance image quality. Among all these techniques, classification plays an important role in computer-aided diagnosis for easy analysis and interpretation of these images. Image classification not only classifies diseases with high accuracy but also analyses which part of the body is infected. The usage of Neural networks classifier in medical imaging applications has opened new doors or opportunities to researchers stirring them to excel in this domain. Moreover, accuracy in clinical practices and the development of more sophisticated equipment are necessary in the medical field for more accurate and quicker decisions. Therefore, keeping this in mind, researchers started using meta-heuristic techniques to classify the methods. This paper provides a brief survey on the role of artificial neural networks in medical image classification, various types of meta-heuristic algorithms applied for optimization purposes, and their hybridization. A comparative analysis showing the effect of applying these algorithms on some classification parameters such as accuracy, sensitivity, and specificity is also provided. From the comparison, it can be observed that the usage of these methods significantly optimizes these parameters leading us to diagnose and treat a number of diseases in their early stage.

Keywords: Medical image classification, feature extraction (FE), Artificial Neural Networks (ANN), classification accuracy, optimization, hybridization, meta-heuristic.

Graphical Abstract
[1]
D.J. Thirumaran, and S. Shylaja, "Medical image processing– A introduction", Int. J. Sci. Res., vol. 4, no. 11, pp. 1197-1199, 2015.
[2]
J.R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective., 4th ed Pearson: Glenview, IL, 2015.
[3]
J.T. Bushberg, and J.M. Boone, The Essential Physics of Medical Imaging., Lippincott Williams and Wilkin, 2011.
[4]
A. Elangovan, and T. Jeyaseelan, "Medical imaging modalities: A survey", Proceedings of 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), 2016pp. 1-4
[5]
P. Smitha, L. Shaji, and M.G. Mini, "A review of medical image classification techniques In", Proceedings of International Conference on VLSI, Communication Instrumentation. 2011, pp. 34-38.
[6]
L. Qin, Q. Zheng, S. Jiang, Q. Huang, and W. Gao, "Unsupervised texture classification: Automatically discover and classify texture patterns", Image Vis. Comput., vol. 26, no. 5, pp. 647-656, 2008.
[7]
A. Materka, and M. Strzelecki, “Texture Analysis Methods - a Review,” Technical Report., Technical University of Lodz, Institute of Electronics, 1998.
[8]
M. Yasmin, M. Sharif, and S. Mohsin, "Neural Networks in Medical Imaging Applications- A Survey", World Appl. Sci. J., vol. 22, no. 1, pp. 85-96, 2013.
[9]
E. Miranda, M. Aryuni, and E. Irwansyah, "A survey of medical image classification techniques", 2016 International Conference on Information Management and Technology (ICIMTech), 2016. Bandung, Indonesia 2016, pp. 56-61.
[http://dx.doi.org/10.1109/ICIMTech.2016.7930302]
[10]
S.A. Lashari, and R. Ibrahim, "A Framework for Medical Images Classification Using Soft Set", Procedia Technol., vol. 11, pp. 548-556, 2013.
[http://dx.doi.org/10.1016/j.protcy.2013.12.227]
[11]
M. Mcauliffe, F. Lalonde, D.P. McGarry, W. Gandler, K. Csaky, and B. Trus, "Medical image processing, analysis & visualizationin clinical research In", Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems. Vol. 14, pp. 381-386, 2001.
[http://dx.doi.org/10.1109/CBMS.2001.941749]
[12]
M. Egmont-Petersen, D. de Ridder, and H. Handels, "Image processing with neural networks—a review", Pattern Recognit., vol. 35, no. 10, pp. 2279-2301, 2002.
[http://dx.doi.org/10.1016/S0031-3203(01)00178-9]
[13]
A.S. Miller, B.H. Blott, and T.K. Hames, "Review of neural network applications in medical imaging and signal processing", Med. Biol. Eng. Comput., vol. 30, no. 5, pp. 449-464, 1992.
[http://dx.doi.org/10.1007/BF02457822] [PMID: 1293435]
[14]
S. Zhenghao, and H. Lifeng, "Application of neural networks in medical image processing", Proceedings of the Second International Symposium on Networking and Network Security, 2010pp. 2-4
[15]
L. Lu, Y. Zheng G., Carneiro and L. Yang, Deep Learning And Convolutional Neural Networks for Medical Image Computing, Springer Nature: Switzerland, 2017.
[http://dx.doi.org/10.1007/978-3-319-42999-1]
[16]
J. Han, J. Pei, and M. Kamber, Data mining: Concepts and techniques., Elsevier, 2011.
[17]
M. Sonka, V. Hlavac, and R. Boyle, Image Pre-Processing.Image Processing, Analysis and Machine Vision, M. Sonka, V. Hlavac, and R. Boyle., Springer: US, 1993, pp. 56-111.
[http://dx.doi.org/10.1007/978-1-4899-3216-7_4]
[18]
J. Leonard, and M.A. Kramer, "Improvement of the back-propagation algorithm for training neural networks", Comput. Chem. Eng., vol. 14, no. 3, pp. 337-341, 1990.
[http://dx.doi.org/10.1016/0098-1354(90)87070-6]
[19]
M.T. Hagan, and M.B. Menhaj, "Training feedforward networks with the Marquardt algorithm", IEEE Trans. Neural Netw., vol. 5, no. 6, pp. 989-993, 1994.
[http://dx.doi.org/10.1109/72.329697] [PMID: 18267874]
[20]
N.B. Karayiannis, and G.W. Mi, "Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques", IEEE Trans. Neural Netw., vol. 8, no. 6, pp. 1492-1506, 1997.
[http://dx.doi.org/10.1109/72.641471] [PMID: 18255750]
[21]
J. Malone, K. McGarry, S. Wermter, and C. Bowerman, "Data mining using rule extraction from Kohonen self-organising maps", Neural Comput. Appl., vol. 15, no. 1, pp. 9-17, 2006.
[http://dx.doi.org/10.1007/s00521-005-0002-1]
[22]
R. Sathya, and A. Abraham, "Comparison of supervised and unsupervised learning algorithms for pattern classification", Int. J. Adv.Res. Artif. Intell., vol. 2, no. 2, pp. 34-38, 2013.
[http://dx.doi.org/10.14569/IJARAI.2013.020206]
[23]
J. Jiang, P. Trundle, and J. Ren, "Medical image analysis with artificial neural networks", Comput. Med. Imaging Graph., vol. 34, no. 8, pp. 617-631, 2010.
[http://dx.doi.org/10.1016/j.compmedimag.2010.07.003] [PMID: 20713305]
[24]
T. Kondo, A.S. Pandya, and J.M. Zurada, "GMDH-type neural networks and their application to the medical image recognition of the lungs In", SICE ’99 Proceedings of the 38th SICE Annual Conference International Session Papers (IEEE Cat. No.99TH8456),. 1999, pp. 1181-1186.
[http://dx.doi.org/10.1109/SICE.1999.788720]
[25]
A. Nejatali, and I.R. Ciric, "An iterative algorithm for electrical impedance imaging using neural networks", IEEE Trans. Magn., vol. 34, no. 5, pp. 2940-2943, 1998.
[http://dx.doi.org/10.1109/20.717686]
[26]
A. Adler, and R. Guardo, "A neural network image reconstruction technique for electrical impedance tomography", IEEE Trans. Med. Imaging, vol. 13, no. 4, pp. 594-600, 1994.
[http://dx.doi.org/10.1109/42.363109] [PMID: 18218537]
[27]
Y.P. Wang, J.W. Dang, Q. Li, and S. Li, "Multimodal medical image fusion using fuzzy radial basis function neural networks In", 2007 International Conference on Wavelet Analysis and Pattern Recognition, pp. 778-782, 2007.
[http://dx.doi.org/10.1109/ICWAPR.2007.4420774]
[28]
I.N. Aizenberg, "Processing of noisy and small-detailed gray-scale images using cellular neural networks", J. Electron. Imaging, vol. 6, no. 3, pp. 272-286, 1997.
[http://dx.doi.org/10.1117/12.269905]
[29]
S.C. Lo, H.P. Chan, J.S. Lin, H. Li, M.T. Freedman, and S.K. Mun, "Artificial convolution neural network for medical image pattern recognition", Neural Netw., vol. 8, no. 7-8, pp. 1201-1214, 1995.
[http://dx.doi.org/10.1016/0893-6080(95)00061-5]
[30]
S. Babaei, and A. Geranmayeh, "Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals", Comput. Biol. Med., vol. 39, no. 1, pp. 8-15, 2009.
[http://dx.doi.org/10.1016/j.compbiomed.2008.10.004] [PMID: 19081085]
[31]
L. Šajn, and M. Kukar, "Image processing and machine learning for fully automated probabilistic evaluation of medical images", Comput. Methods Programs Biomed., vol. 104, no. 3, pp. e75-e86, 2011.
[http://dx.doi.org/10.1016/j.cmpb.2010.06.021] [PMID: 20846741]
[32]
K. Suzuki, S.G. Armato III, F. Li, S. Sone, and K. Doi, "Massive Training Artificial Neural Network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography", Med. Phys., vol. 30, no. 7, pp. 1602-1617, 2003.
[http://dx.doi.org/10.1118/1.1580485] [PMID: 12906178]
[33]
G. Schaefer, B. Krawczyk, M.E. Celebiand, and H. Iyatomi, "An ensemble classification approach for melanoma diagnosis", Memetic. Comput., vol. 6, no. 4, pp. 233-240, 2014.
[34]
D.M. Hawkins, "The problem of overfitting", J. Chem. Inf. Comput. Sci., vol. 44, no. 1, pp. 1-12, 2004.
[http://dx.doi.org/10.1021/ci0342472] [PMID: 14741005]
[35]
P.J. Werbos, Beyond Regression: New tools for prediction and analysis in the behavioral sciences, Ph.D. Thesis - Harvard University, 1974.
[36]
O.C. Chen, and B.J. Sheu, "Optimization schemes for neural network training", In", Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94). Vol. 2, pp. 817-822, 1994.
[http://dx.doi.org/10.1109/ICNN.1994.374284]
[37]
A. Charnes, W. Cooper, A.Y. Lewin, and L.M. Seiford, "Data envelopment analysis theory, methodology and applications", J. Oper. Res. Soc., vol. 48, no. 3, pp. 332-333, 1997.
[http://dx.doi.org/10.1057/palgrave.jors.2600342]
[38]
D.W. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters", J. Soc. Ind. Appl. Math., vol. 11, no. 2, pp. 431-441, 1963.
[http://dx.doi.org/10.1137/0111030]
[39]
V.K. Ojha, A. Abraham, and V. Snášel, "Metaheuristic design of feedforward neural networks: A review of two decades of research", Eng. Appl. Artif. Intell., vol. 60, pp. 97-116, 2017.
[http://dx.doi.org/10.1016/j.engappai.2017.01.013]
[40]
X.S. Yang, Nature-Inspired Meta-heuristic Algorithms., Luniver Press, 2008.
[41]
A.H. Gandomi, X.S. Yang, S. Talatahari, and A.H. Alavi, Meta-heuristic Algorithms in Modeling and Optimization.Meta-heuristic Applications in Structures and Infrastructures., Elsevier, 2013, pp. 1-24.
[42]
M. Crepinšek, S.H. Liu, and M. Mernik, "Exploration and exploitation in evolutionary algorithms: A survey", Computing Surveys (CSUR), vol. 45, no. 3, pp. 1-33, 2013. [CSUR
[43]
N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller, "Equation of state calculations by fast computing machines", J. Chem. Phys., vol. 21, no. 6, pp. 1087-1092, 1953.
[http://dx.doi.org/10.1063/1.1699114]
[44]
X.S. Yang, and X. He, "Bat algorithm: Literature review and applications", Int. J. Bio-inspired Comput., vol. 5, no. 3, pp. 141-149, 2013.
[http://dx.doi.org/10.1504/IJBIC.2013.055093]
[45]
S. Roy, and S.S. Chaudhuri, "Cuckoo search algorithm using Lévy flight: A review", Int. J. Mod. Educ. Comput. Sci., vol. 5, no. 12, pp. 1-10, 2013.
[http://dx.doi.org/10.5815/ijmecs.2013.12.02]
[46]
J.T. Oliva, H.D. Lee, N. Spolaôr, C.S. Coy, and F.C. Wu, "Prototype system for feature extraction, classification and study of medical images", Expert Syst. Appl., vol. 63, pp. 267-283, 2016.
[http://dx.doi.org/10.1016/j.eswa.2016.07.008]
[47]
X.S. Yang, Engineering optimization: An introduction with meta-heuristic applications., John Wiley & Sons, 2010.
[http://dx.doi.org/10.1002/9780470640425]
[48]
C. Blum, and A. Roli, "Meta-heuristics in combinatorial optimization: Overview and conceptual comparison", ACM Comput. Surv., vol. 35, no. 3, pp. 268-308, 2003.
[http://dx.doi.org/10.1145/937503.937505]
[49]
S. Binitha, and S.S. Sathya, "A survey of bio inspired optimization algorithms", Int. J. Soft Computing Eng., vol. 2, pp. 137-151, 2012.
[50]
A. Zhou, B.Y. Qu, H. Li, S.Z. Zhoa, P.N. Suganthan, and Q. Zhang, "Multi-objective evolutionary algorithms: A survey of the state of the art", Swarm Evol. Comput., vol. 1, no. 1, pp. 32-49, 2011.
[http://dx.doi.org/10.1016/j.swevo.2011.03.001]
[51]
L. Bianchi, M. Dorigo, L.M. Gambardella, and W.J. Gutjahr, "A survey on metaheuristics for stochastic combinatorial optimization", Nat. Comput., vol. 8, no. 2, pp. 239-287, 2009.
[http://dx.doi.org/10.1007/s11047-008-9098-4]
[52]
C.W. Tsai, M.C. Chiang, A. Ksentini, and M. Chen, "Meta-heuristic algorithms for healthcare: Open issues and challenges", Comput. Electr. Eng., vol. 53, pp. 421-434, 2016.
[http://dx.doi.org/10.1016/j.compeleceng.2016.03.005]
[53]
K. Rao, P. P. Chand, and M. V. Murthy, "A neural network approach in medical decision systems", J. Theoretical Appl. Inf. Technol.. Vol. 3, No. 4, Dec 2007.
[54]
B. Sahiner, H.P. Chan, N. Petrick, D. Wei, M.A. Helvie, D.D. Adler, and M.M. Goodsitt, "Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images", IEEE Trans. Med. Imaging, vol. 15, no. 5, pp. 598-610, 1996.
[http://dx.doi.org/10.1109/42.538937] [PMID: 18215941]
[55]
W.E. Reddick, J.O. Glass, E.N. Cook, T.D. Elkin, and R.J. Deaton, "Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks", IEEE Trans. Med. Imaging, vol. 16, no. 6, pp. 911-918, 1997.
[http://dx.doi.org/10.1109/42.650887] [PMID: 9533591]
[56]
J. Khan, J.S. Wei, M. Ringnér, L.H. Saal, M. Ladanyi, F. Westermann, and F. Berthold, "M. Schwab C. R. Antonescu, C. Peterson and P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks", Nat. Med., vol. 7, no. 6, pp. 673-679, 2001.
[http://dx.doi.org/10.1038/89044] [PMID: 11385503]
[57]
Z.H. Zhou, Y. Jiang, Y.B. Yang, and S.F. Chen, "Lung cancer cell identification based on artificial neural network ensembles", Artif. Intell. Med., vol. 24, no. 1, pp. 25-36, 2002.
[http://dx.doi.org/10.1016/S0933-3657(01)00094-X] [PMID: 11779683]
[58]
R. Dua, D.G. Beetner, W.V. Stoecker, and D.C. Wunsch, "Detection of basal cell carcinoma using electrical impedance and neural networks", IEEE Trans. Biomed. Eng., vol. 51, no. 1, pp. 66-71, 2004.
[http://dx.doi.org/10.1109/TBME.2003.820387] [PMID: 14723495]
[59]
F. Gorunescu, M. Gorunescu, E. El-Darzi, and S. Gorunescu, "An evolutionary computational approach to probabilistic neural network with application to hepatic cancer diagnosis", 18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05), 2005pp. 461-466
[http://dx.doi.org/10.1109/CBMS.2005.24]
[60]
P. Georgiadis, D. Cavouras, I. Kalatzis, A. Daskalakis, G.C. Kagadis, K. Sifaki, M. Malamas, G. Nikiforidis, and E. Solomou, "Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features", Comput. Methods Programs Biomed., vol. 89, no. 1, pp. 24-32, 2008.
[http://dx.doi.org/10.1016/j.cmpb.2007.10.007] [PMID: 18053610]
[61]
B. Golosio, G.L. Masala, A. Piccioli, P. Oliva, M. Carpinelli, R. Cataldo, P. Cerello, F. De Carlo, F. Falaschi, M.E. Fantacci, G. Gargano, P. Kasae, and M. Torsello, "A novel multithreshold method for nodule detection in lung CT", Med. Phys., vol. 36, no. 8, pp. 3607-3618, 2009.
[http://dx.doi.org/10.1118/1.3160107] [PMID: 19746795]
[62]
A. Retico, B. Francesco, N. Camarlinghi, C. Carpentieri, M.E. Fantacci, and I. Gori, "“A Voxel-Based Neural Approach (VBNA) to identify lung nodules in the ANODE09 study,” In Medical Imaging 2009: Computer-Aided Diagnosis", International Society for Optics and Photonics, vol. 7260, p. 72601S, 2009.
[http://dx.doi.org/10.1117/12.811721]
[63]
A. Khemphila, and V. Boonjing, "Heart disease classification using neural network and feature selection In", 2011 21st IEEE International Conference on Systems Engineering. 2011, pp. 406-409.
[http://dx.doi.org/10.1109/ICSEng.2011.80]
[64]
A. Andrade, J.S. Silva, J. Santos, and P. Belo-Soares, "Classifier approaches for liver steatosis using ultrasound images", Procedia Technol., vol. 5, pp. 763-770, 2012.
[http://dx.doi.org/10.1016/j.protcy.2012.09.084]
[65]
R. Tomari, W.N. Zakaria, M.M. Jamil, F.M. Nor, and N.F. Fuad, "Computer aided system for red blood cell classification in blood smear image", Procedia Comput. Sci., vol. 42, pp. 206-213, 2014.
[http://dx.doi.org/10.1016/j.procs.2014.11.053]
[66]
B. Van Ginneken, A.A. Setio, C. Jacobs, and F. Ciompi, "Off-theshelf convolutional neural network features for pulmonary nodule detection in computed tomography scans", In", 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI),. 2015, pp. 286-289.
[http://dx.doi.org/10.1109/ISBI.2015.7163869]
[67]
M.J. Van Grinsven, B. Van Ginneken, C.B. Hoyng, T. Theelen, and C.I. Sanchez, "Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1273-1284, 2016.
[http://dx.doi.org/10.1109/TMI.2016.2526689] [PMID: 26886969]
[68]
Q. Dou, H. Chen, L. Yu, L. Zhao, J. Qin, D. Wang, V.C. Mok, L. Shi, and P.A. Heng, "Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1182-1195, 2016.
[http://dx.doi.org/10.1109/TMI.2016.2528129] [PMID: 26886975]
[69]
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, "Lung pattern classification for interstitial lung diseases using a deep convolutional neural network", IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1207-1216, 2016.
[http://dx.doi.org/10.1109/TMI.2016.2535865] [PMID: 26955021]
[70]
T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Polónia, and A. Campilho, "Classification of breast cancer histology images using Convolutional neural Networks", PLoS One, vol. 12, no. 6, p. e0177544, 2017.
[http://dx.doi.org/10.1371/journal.pone.0177544] [PMID: 28570557]
[71]
J.H. Holland, "Genetic algorithms", Sci. Am., vol. 267, no. 1, pp. 66-73, 1992.
[http://dx.doi.org/10.1038/scientificamerican0792-66]
[72]
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning., 1st ed Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1989.
[73]
A. Ghaheri, S. Shoar, M. Naderan, and S.S. Hoseini, "The applications of genetic algorithms in medicine", Oman Med. J., vol. 30, no. 6, pp. 406-416, 2015.
[http://dx.doi.org/10.5001/omj.2015.82] [PMID: 26676060]
[74]
B. Sahiner, H.P. Chan, and D. Wei, "N. Petrick M. A. Helvie, D. D. Adler and M. M. Goodsitt, “Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue", Med. Phys., vol. 23, no. 10, pp. 1671-1684, 1996.
[http://dx.doi.org/10.1118/1.597829] [PMID: 8946365]
[75]
M.A. Anastasio, H. Yoshida, R. Nagel, R.M. Nishikawa, and K. Doi, "A genetic algorithm-based method for optimizing the performance of a computer-aided diagnosis scheme for detection of clustered microcalcifications in mammograms", Med. Phys., vol. 25, no. 9, pp. 1613-1620, 1998.
[http://dx.doi.org/10.1118/1.598341] [PMID: 9775365]
[76]
M.A. Kupinski, and M.A. Anastasio, "Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves", IEEE Trans. Med. Imaging, vol. 18, no. 8, pp. 675-685, 1999.
[http://dx.doi.org/10.1109/42.796281] [PMID: 10534050]
[77]
M. Gletsos, and S.G. Mougiakakou, "G. K. Matsopoulos K. S. Nikita, A. S. Nikita and D. Kelekis, “A computer-aided diagnostic system to characterize CT focal liver lesions: Design and optimization of a neural network classifier", IEEE Trans. Inf. Technol. Biomed., vol. 7, no. 3, pp. 153-162, 2003.
[http://dx.doi.org/10.1109/TITB.2003.813793] [PMID: 14518728]
[78]
D.Y. Tsai, Y. Lee, M. Sekiya, and M. Ohkubo, "Medical image classification using genetic-algorithm based fuzzy-logic approach", J. Electron. Imaging, vol. 13, pp. 780-788, 2004.
[http://dx.doi.org/10.1117/1.1786607]
[79]
V. Bevilacqua, G. Mastronardi, F. Menolascina, P. Pannarale, and A. Pedone, "A novel multi-objective genetic algorithm approach to artificial neural network topology optimization: The breast cancer classification problem", The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006pp. 1958-1965
[80]
A.G. Karegowda, A.S. Manjunath, and M.A. Jayaram, "Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes", Int. J. Soft Comput., vol. 2, no. 2, pp. 15-23, 2011.
[http://dx.doi.org/10.5121/ijsc.2011.2202]
[81]
B. Pandey, T. Jain, V. Kothari, and T. Grover, "Evolutionary modular neural network approach for breast cancer diagnosis", Int. J. Comput. Sci. Issu., vol. 9, no. 1, pp. 219-225, 2012.
[82]
L. A. Lim, R. N. Maguib, E. P. Dadios, and J. M. Avila, "Implementation of GA-KSOM and ANFIS in the classification of colonic histopathological images", In", TENCON 2012 IEEE Region 10 Conference. 2012, pp. 1-5.
[83]
M.M. Mohammed, A. Badr, and M.B. Abdelhalim, "Image classification and retrieval using optimized pulse-coupled neural network", Expert Syst. Appl., vol. 42, no. 11, pp. 4927-4936, 2015.
[http://dx.doi.org/10.1016/j.eswa.2015.02.019]
[84]
A. Mehta, A.S. Parihar, and N. Mehta, "Supervised classification of dermoscopic images using optimized fuzzy clustering based multilayer feed-forward neural network In", 2015 International Conference on Computer, Communication and Control (IC4),, p. pp. 1-6., 2015.
[http://dx.doi.org/10.1109/IC4.2015.7375719]
[85]
A. Bhardwaj, and A. Tiwari, "Breast cancer diagnosis using genetically optimized neural network model", Expert Syst. Appl., vol. 42, no. 10, pp. 4611-4620, 2015.
[http://dx.doi.org/10.1016/j.eswa.2015.01.065]
[86]
J. Kennedy, and R. Eberhart, "Particle swarm optimization", IEEE Proceedings of ICNN'95-International Conference on Neural Networks. Vol. 4, pp. 1942-1948, 1995.
[http://dx.doi.org/10.1109/ICNN.1995.488968]
[87]
M. Settles, "An introduction to particle swarm optimization", Department of Computer Science, University of Idaho, vol. 2, p. 8, 2005.
[88]
R.C. Eberhart, and Y. Shi, "Particle swarm optimization: developments, applications and resources", In", Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). Vol. 1, pp. 81-86, 2001.
[http://dx.doi.org/10.1109/CEC.2001.934374]
[89]
X. Niu, Y. Qiu, S. Tong, and Y. Zhu, "Application of particle swarm system as a novel parameter optimization technique on spatiotemporal retina model", In", 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, p. pp. 5795-5798, 2007.
[http://dx.doi.org/10.1109/IEMBS.2007.4353664]
[90]
Y.D. Zhang, S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO", Prog. Electromagnetics Res., vol. 109, pp. 325-343, 2010.
[http://dx.doi.org/10.2528/PIER10090105]
[91]
D.J. Hemanth, C.K. Vijila, and J. Anitha, "Performance improved PSO based modified counter propagation neural network for abnormal MR brain image classification", Int. J. Advance. Soft Comput, vol. 2, no. 1, pp. 65-84, 2010.
[92]
S.N. Qasem, and S.M. Shamsuddin, "Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis", Appl. Soft Comput., vol. 11, no. 1, pp. 1427-1438, 2011.
[http://dx.doi.org/10.1016/j.asoc.2010.04.014]
[93]
J. Dheeba, and S.T. Selvi, "A swarm optimized neural network system for classification of microcalcification in mammograms", J. Med. Syst., vol. 36, no. 5, pp. 3051-3061, 2012.
[http://dx.doi.org/10.1007/s10916-011-9781-3] [PMID: 21947904]
[94]
S. Dehuri, R. Roy, S.B. Cho, and A. Ghosh, "An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification", J. Syst. Softw., vol. 85, no. 6, pp. 1333-1345, 2012.
[http://dx.doi.org/10.1016/j.jss.2012.01.025]
[95]
J. Dheeba, N.A. Singh, and S.T. Selvi, "Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach", J. Biomed. Inform., vol. 49, pp. 45-52, 2014.
[http://dx.doi.org/10.1016/j.jbi.2014.01.010] [PMID: 24509074]
[96]
D.J. Hemanth, J. Anitha, and V.E. Balas, "Performance improved hybrid intelligent system for medical image classification In", Proceedings of the 7th Balkan Conference on Informatics Conference, pp. 1-5, 2015.
[http://dx.doi.org/10.1145/2801081.2801095]
[97]
E.E. Nithila, and S.S. Kumar, "Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images", Eng. Sci. Technol. Int. J., vol. 20, no. 3, pp. 1192-1202, 2017.
[http://dx.doi.org/10.1016/j.jestch.2016.12.006]
[98]
M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: Optimization by a colony of cooperating agents", IEEE Trans. Syst. Man Cybern. B Cybern., vol. 26, no. 1, pp. 29-41, 1996.
[http://dx.doi.org/10.1109/3477.484436] [PMID: 18263004]
[99]
K. Socha, and C. Blum, "An ant colony optimization algorithm for continuous optimization: Application to feed-forward neural network training", Neural Comput. Appl., vol. 16, no. 3, pp. 235-247, 2007.
[http://dx.doi.org/10.1007/s00521-007-0084-z]
[100]
M. Karnan, K. Thangavel, R. Sivakuar, and K. Geetha, "Ant colony optimization for feature selection and classification of microcalcifications in digital mammograms In", 2006 International Conference on Advanced Computing and Communications, 2006pp. 298-303
[http://dx.doi.org/10.1109/ADCOM.2006.4289903]
[101]
R.K. Sivagaminathan, and S. Ramakrishnan, "A hybrid approach for feature subset selection using neural networks and ant colony optimization", Expert Syst. Appl., vol. 33, no. 1, pp. 49-60, 2007.
[http://dx.doi.org/10.1016/j.eswa.2006.04.010]
[102]
V. Soleimani, and F.H. Vincheh, "Improving ant colony optimization for brain MRI image segmentation and brain tumor diagnosis In", 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA), pp. 1-6, 2013.
[http://dx.doi.org/10.1109/PRIA.2013.6528454]
[103]
T.T. Erguzel, S. Ozekes, S. Gultekin, and N. Tarhan, "Ant colony optimization based feature selection method for QEEG data classification", Psychiatry Investig., vol. 11, no. 3, pp. 243-250, 2014.
[http://dx.doi.org/10.4306/pi.2014.11.3.243] [PMID: 25110496]
[104]
A.E. Hassanien, "H. M. Moftah A. T. Azar and M. Shoman, “MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier", Appl. Soft Comput., vol. 14, pp. 62-71, 2014.
[http://dx.doi.org/10.1016/j.asoc.2013.08.011]
[105]
G.S. Raghtate, and S.S. Salankar, "Automatic brain MRI classification using modified ant colony system and neural network classifier In", 2015 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 1241-1246, 2015.
[http://dx.doi.org/10.1109/CICN.2015.239]
[106]
D. Karaboga, B. Akay, and C. Ozturk, Artificial Bee Colony (ABC) optimization algorithm for training feed-forward neural Networks., Modeling Decisions for Artificial Intelligence, 2007, pp. 318-329.
[http://dx.doi.org/10.1007/978-3-540-73729-2_30]
[107]
D. Karaboga, and B. Basturk, "Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems In", International Fuzzy Systems Association World Congress, 2007pp. 789-798
[http://dx.doi.org/10.1007/978-3-540-72950-1_77]
[108]
L. Wu, and S. Wang, "Magnetic resonance brain image classification by an improved artificial bee colony algorithm", Prog. Electromagnetics Res., vol. 116, pp. 65-79, 2011.
[109]
M. Schiezaro, and H. Pedrini, "Data feature selection based on artificial bee colony algorithm", EURASIP J. Image Video Process., vol. 2013, no. 1, pp. 1-8, 2013.
[http://dx.doi.org/10.1186/1687-5281-2013-47]
[110]
D. Janaki Sathya, and K.G. Arulmani, "Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm", Sci. Asia, vol. 39, no. 3, pp. 294-305, 2013.
[http://dx.doi.org/10.2306/scienceasia1513-1874.2013.39.294]
[111]
M. Pourmandi, and J. Addeh, "Breast cancer diagnosis using fuzzy feature and optimized neural network via the Gbest-guided artificial bee colony algorithm", Comput. Res. Prog. Appli. Sci. Eng., vol. 1, no. 4, pp. 152-159, 2015.
[112]
A. Singh, and D. Kumar, "Novel ABC based training algorithm for ovarian cancer detection using neural network In", 2017 International Conference on Trends in Electronics and Informatics (ICEI), pp. 594-597, 2017.
[http://dx.doi.org/10.1109/ICOEI.2017.8300771]
[113]
X.S. Yang, "Cuckoo search via levy flights", 2009 World Congress on Nature Biologically Inspired Computing (NaBIC), 2009pp. 210-214
[114]
E. Valian, S. Mohanna, and S. Tavakoli, "Improved cuckoo search algorithm for feed-forward neural network training", International Journal of Artificial Intelligence & Applications, vol. 2, no. 3, pp. 36-43, 2011.
[http://dx.doi.org/10.5121/ijaia.2011.2304]
[115]
V. Tiwari, "Face recognition based on cuckoo search algorithm", Image (IN), vol. 7, no. 8, pp. 401-405, 2012.
[116]
D.K. Nagthane, and A.M. Rajurkar, "Cuckoo search : An optimized way for mammogram feature selection", Intl. J. Curr. Eng. Scientific Res., vol. 4, no. 8, pp. 81-86, 2017.
[117]
D.K. Priya, B.B. Sam, S. Lavanya, and A.P. Sajin, "A survey on medical image denoising using optimisation technique and classification In", 2017 International Conference on Information Communication and Embedded Systems (ICICES), pp. 1-6, 2017.
[http://dx.doi.org/10.1109/ICICES.2017.8070729]
[118]
S. Mirjalili, S.M. Mirjalili, and A. Lewis, "Grey wolf optimizer", Adv. Eng. Software, vol. 69, pp. 46-61, 2014.
[http://dx.doi.org/10.1016/j.advengsoft.2013.12.007]
[119]
S. Mirjalili, "How effective is the grey wolf optimizer in training multi-layer perceptrons", Appl. Intell., vol. 43, no. 1, pp. 150-161, 2015.
[http://dx.doi.org/10.1007/s10489-014-0645-7]
[120]
A. Parsian, M. Ramezani, and N. Ghadimi, "A hybrid neural network-gray wolf optimization algorithm for melanoma detection", Biomed. Res. (Aligarh), vol. 28, no. 8, pp. 3408-3411, 2017.
[121]
A. Sahoo, and S. Chandra, "Multi-objective grey wolf optimizer for improved cervix lesion classification", Appl. Soft Comput., vol. 52, pp. 64-80, 2017.
[http://dx.doi.org/10.1016/j.asoc.2016.12.022]
[122]
P. Raju, V.M. Rao, and B.P. Rao, "Grey wolf optimization-based artificial neural network for classification of kidney images", J. Circuits Syst. Comput., vol. 27, no. 14, p. 1850231, 2018.
[http://dx.doi.org/10.1142/S0218126618502316]
[123]
G.D. Magoulas, V.P. Plagianakos, and M.N. Vrahatis, "Neural network-based colonoscopic diagnosis using on-line learning and differential evolution", Appl. Soft Comput., vol. 4, no. 4, pp. 369-379, 2004.
[http://dx.doi.org/10.1016/j.asoc.2004.01.005]
[124]
A.O. Ibrahim, S.M. Shamsuddin, A.Y. Saleh, A. Abdelmaboud, and A. Ali, "Intelligent multi-objective classifier for breast cancer diagnosis based on multilayer perceptron neural network and differential evolution", 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015pp. 422-427
[http://dx.doi.org/10.1109/ICCNEEE.2015.7381405]
[125]
H.T. Thein, and K.M. Tun, "An approach for breast cancer diagnosis classification using neural network", Adv. Comput. Int. J., vol. 6, pp. 1-11, 2015.
[http://dx.doi.org/10.5121/acij.2015.6101]
[126]
J. Dheeba, and S.T. Selvi, "An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network", J. Med. Syst., vol. 36, no. 5, pp. 3223-3232, 2012.
[http://dx.doi.org/10.1007/s10916-011-9813-z] [PMID: 22173907]
[127]
B. Cigale, M. Divjak, and D. Zazula, "Application of simulated annealing to biosignal classification and segmentation In", Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002), pp. 165-170, 2002.
[http://dx.doi.org/10.1109/CBMS.2002.1011372]
[128]
J.P. Yusiong, "Optimizing artificial neural networks using cat swarm optimization algorithm", Int. J. Intell. Syst. Appl., vol. 5, no. 1, p. 69, 2012.
[http://dx.doi.org/10.5815/ijisa.2013.01.07]
[129]
D.T. Sarabai, and K. Arthi, "Efficient breast cancer classification using improved fuzzy cognitive maps with Csonn", Int. J. Appl. Eng. Res, vol. 11, no. 4, pp. 2478-2485, 2016.
[130]
Y.D. Zhang, Y. Sui, J. Sun, G. Zhao, and P. Qian, "Cat swarm optimization applied to alcohol use disorder identification", Multimedia Tools Appl., vol. 77, no. 17, pp. 22875-22896, 2018.
[http://dx.doi.org/10.1007/s11042-018-6003-8]
[131]
M.R. Senapatand, and P.K. Dash, "Local linear wavelet neural network based breast tumor classification using firefly algorithm", Neural Comput. Appl., vol. 22, no. 7, pp. 1591-1598, 2013.
[132]
J. Nahar, T. Imam, K.S. Tickle, A.S. Ali, and Y.P. Chen, "Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer", Expert Syst. Appl., vol. 39, no. 16, pp. 12371-12377, 2012.
[http://dx.doi.org/10.1016/j.eswa.2012.04.045]
[133]
P. Kora, and S.R. Kalva, "Improved bat algorithm for the detection of myocardial infarction", Springerplus, vol. 4, no. 1, pp. 1-8, 2015.
[http://dx.doi.org/10.1186/s40064-015-1379-7] [PMID: 26558169]
[134]
S. Rattan, S. Kaur, N. Kansal, and J. Kaur, "An optimized lung cancer classification system for computed tomography images In", 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1-6, 2017.
[http://dx.doi.org/10.1109/ICIIP.2017.8313676]
[135]
R.G. Raidl, "A unified view on hybrid meta-heuristics", International Workshop on Hybrid Meta-heuristics. Springer, Berlin, Heidelberg, 2006, pp. 1-12.
[136]
C. Blum, J. Puchinger, G.R. Raidl, and A. Roli, "A brief survey on hybrid meta-heuristics", Proceedings of BIOMA, 2010pp. 3-18
[137]
K. Thangavel, M. Karnan, R. Sivakumar, and A. K. Mohideen, "Ant colony system for segmentation and classification of microcalcification in mammograms", Int. J. Artif. Intell. Mach. Learn., p. pp. 298-303.
[138]
K. Geetha, and K. Thanushkodi, "New particle swarm optimization for feature selection and classification of micro-calcifications in mammograms", 2008 International Conference on Signal Processing, Communications and Networking, 2008pp. 458-463
[139]
M. Suganthi, and M. Madheswaran, "An improved medical decision support system to identify the breast cancer using mammogram", J. Med. Syst., vol. 36, no. 1, pp. 79-91, 2012.
[http://dx.doi.org/10.1007/s10916-010-9448-5] [PMID: 20703746]
[140]
S. Saraswathi, B.S. Mahanand, A. Kloczkowski, S. Suresh, and N. Sundararajan, "Detection of onset of Alzheimer’s disease from MRI images using a GA-ELM-PSO classifier In", 2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), 2013pp. 42-48
[http://dx.doi.org/10.1109/CIMI.2013.6583856]
[141]
G. Jothi, H.H. Inbarani, and A.T. Azar, "Hybrid tolerance rough set: PSO based supervised feature selection for digital mammogram images", Int. J. Fuzzy Syst. Appl. IJFSA, vol. 3, no. 4, pp. 15-30, 2013.
[http://dx.doi.org/10.4018/ijfsa.2013100102]
[142]
G. Jothi, and H. Inbarani, "Hybrid tolerance rough set-firefly based supervised feature selection for MRI brain tumor image classification", Appl. Soft Comput., vol. 46, pp. 639-651, 2016.
[http://dx.doi.org/10.1016/j.asoc.2016.03.014]
[143]
N.K. Behera, A.R. Routray, J. Nayak, and H.S. Behera, "Bird mating optimization based multilayer perceptron for diseases classification", Comput. Intell. Data Min., vol. 3, pp. 305-315, 2015.
[http://dx.doi.org/10.1007/978-81-322-2202-6_27]
[144]
S. Wang, Y. Zhang, Z. Dong, S. Du, G. Ji, J. Yan, J. Yang, Q. Wang, C. Feng, and P. Phillips, "Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection", Int. J. Imaging Syst. Technol., vol. 25, no. 2, pp. 153-164, 2015.
[http://dx.doi.org/10.1002/ima.22132]
[145]
Y. Zhang, S. Wang, Z. Dong, P. Phillip, G. Ji, and J. Yang, "Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization", Prog. Electromagnetics Res., vol. 152, pp. 41-58, 2015.
[http://dx.doi.org/10.2528/PIER15040602]
[146]
G.T. Reddy, and N. Khare, "Hybrid firefly-bat optimized fuzzy artificial neural network based classifier for diabetes diagnosis", Int. J. Intell. Eng. Syst., vol. 10, no. 4, pp. 18-27, 2017.
[http://dx.doi.org/10.22266/ijies2017.0831.03]
[147]
Q. Li, H. Chen, H. Huang, X. Zhao, Z. Cai, C. Tong, W. Liu, and X. Tian, "An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis", Comput. Math. Methods Med., vol. 2017, p. 15, 2017.
[http://dx.doi.org/10.1155/2017/9512741] [PMID: 28246543]
[148]
S. Sudha, and D.M. Ezhilarasi, "Prediction of liver disorder using neuro-fuzzy system and chicken swarm optimization algorithm for ultrasound image", TAGA J., vol. 14, pp. 575-595, 2018.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy