Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

[1] Fusco R, Sansone M, Petrillo A, Sansone C. A multiple classifier system for classification of breast lesions using dynamic and morphological features in DCE-MRI. Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) 2012 Nov 7 (pp. 684-692). Springer Berlin Heidelberg.10.1007/978-3-642-34166-3_75Search in Google Scholar

[2] Weszka JS. A survey of threshold selection techniques. Computer Graphics and Image Processing. 1978;7(2):259-265.10.1016/0146-664X(78)90116-8Open DOISearch in Google Scholar

[3] Sathya J, Geetha K. Experimental investigation of classification algorithms for predicting lesion type on breast DCE-MR images. Int J Comp App. 2013;82(4):1-8.10.5120/14101-2125Search in Google Scholar

[4] Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med. 2003;50(1):92-98.10.1002/mrm.1049612815683Open DOISearch in Google Scholar

[5] Degenhard A, Tanner C, Hayes C, et al. Comparison between radiological and artificial neural network diagnosis in clinical screening. Physiol Meas. 2002;23(4):727-739.10.1088/0967-3334/23/4/31112450272Open DOISearch in Google Scholar

[6] Iftikhar K, Anwar S, Khan MT, Akbar SR. An Optimal Neural Network Based Classification Technique for Breast Cancer Detection. Journal of Engineering and Applied Sciences (JEAS), University of Engineering and Technology, Peshawar. 2016;35(1):51-58.Search in Google Scholar

[7] Tzacheva AA, Najarian K, Brockway JP. Breast cancer detection in gadolinium-enhanced MR images by static region descriptors and neural networks. J Magn Reson Imaging. 2003;17(3):337-342.10.1002/jmri.1025912594724Open DOISearch in Google Scholar

[8] Meinel LA, Stolpen AH, Berbaum KS, et al. Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging. 2007;25(1):89-95.10.1002/jmri.2079417154399Search in Google Scholar

[9] Keyvanfard F, Shoorehdeli MA, Teshnehlab M. Feature selection and classification of breast cancer on dynamic magnetic resonance imaging using ANN and SVM. American Journal of Biomedical Engineering. 2011;1(1):20-25.10.5923/j.ajbe.20110101.04Search in Google Scholar

[10] Juntu J, Sijbers J, De Backer S, et al. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging. 2010;31(3):680-689.10.1002/jmri.2209520187212Search in Google Scholar

[11] Sathya J, Geetha K. Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm. Science Asia. 2013;39(3):294-305.10.2306/scienceasia1513-1874.2013.39.294Search in Google Scholar

[12] Sawant HK, Shinde VD. Breast tumor analysis using texture features and wavelet transform with dynamic neural network based training. J Inf Knowl Res Comput Eng. 2011;1(2):46-51.Search in Google Scholar

[13] Agner SC, Soman S, Libfeld E, et al. Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging. 2011;24(3):446-463.10.1007/s10278-010-9298-1309205520508965Open DOISearch in Google Scholar

[14] Vomweg TW, Buscema M, Kauczor HU, et al. Improved artificial neural networks in prediction of malignancy of lesions in contrast-enhanced MR-mammography. Med Phys. 2003;30(9):2350-2359.10.1118/1.160087114528957Open DOISearch in Google Scholar

[15] Bhooshan N, Giger ML, Jansen SA, et al. Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology. 2010;254(3):680-690.10.1148/radiol.09090838282669520123903Search in Google Scholar

[16] Sathya DJ, Geetha K. Development of Intelligent System Based on Artificial Swarm Bee Colony Clustering Algorithm for Efficient Mass Extraction from Breast DCE-MR Images. Int J on Recent Trends in Engineering and Technology. 2011;6(1):82-88.Search in Google Scholar

[17] Woods BJ, Clymer BD, Kurc T, et al. Malignant-lesion segmentation using 4D co-occurrence texture analysis applied to dynamic contrast-enhanced magnetic resonance breast image data. J Magn Reson Imaging. 2007;25(3):495-501.10.1002/jmri.2083717279534Open DOISearch in Google Scholar

[18] Gal Y, Mehnert A, Bradley A, et al. Feature and classifier selection for automatic classification of lesions in dynamic contrast-enhanced MRI of the breast. In: Digital Image Computing: Techniques and Applications, 2009. DICTA'09. 2009 Dec 1 (pp. 132-139). IEEE.10.1109/DICTA.2009.29Search in Google Scholar

[19] Yao J, Chen J, Chow C. Breast tumor analysis in dynamic contrast enhanced MRI using texture features and wavelet transform. IEEE Journal of Selected Topics in Signal Processing. 2009;3(1):94-100.10.1109/JSTSP.2008.2011110Open DOISearch in Google Scholar

[20] Loose J, Harz MT, Laue H, et al. Assessment of texture analysis on DCE-MRI data for the differentiation of breast tumor lesions. In: SPIE Medical Imaging 2009 Feb 26 (pp. 72600K-72600K). International Society for Optics and Photonics.10.1117/12.812971Search in Google Scholar

[21] Haralick RM, Shanmugam K. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;3(6):610-621.Search in Google Scholar

[22] Vickers AJ. Parametric versus non-parametric statistics in the analysis of randomized trials with non-normally distributed data. BMC Med Res Methodol. 2005;35(5):1-12.10.1186/1471-2288-5-35131053616269081Search in Google Scholar

[23] Haury AC, Gestraud P, Vert JP. The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PloS One. 2011;6(12):e28210.10.1371/journal.pone.0028210324438922205940Search in Google Scholar

[24] Foody GM. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment. 2009;113(8):1658-1663.10.1016/j.rse.2009.03.014Search in Google Scholar

[25] Liu H, Li J, Wong L. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Inform. 2002;13:51-60.Search in Google Scholar

[26] Satake H, Nishio A, Ikeda M, et al. Predictive value for malignancy of suspicious breast masses of BI-RADS categories 4 and 5 using ultrasound elastography and MR diffusion-weighted imaging. AJR Am J Roentgenol. 2011;196(1):202-209.10.2214/AJR.09.410821178068Search in Google Scholar

[27] Eker OF, Camci F, Kumar U. Failure diagnostics on railway turnout systems using support vector machines. In: International Workshop and Congress on eMaintenance: 22/06/2010-24/06/2010 2010 (pp. 248-251). Luleå tekniska universitet.Search in Google Scholar

[28] Li XL, Shao ZJ, Qian JX. An optimizing method based on autonomous animats: fish-swarm algorithm. System Engineering Theory and Practice. 2002;22(11):32-38.Search in Google Scholar

[29] de Oliveira J, Ludermir T. A modified artificial fish swarm algorithm for the optimization of extreme learning machines. Artificial Neural Networks and Machine Learning–ICANN 2012. 2012:66-73.10.1007/978-3-642-33266-1_9Search in Google Scholar

[30] Chen H, Wang S, Li J, Li Y. A hybrid of artificial fish swarm algorithm and particle swarm optimization for feed forward neural network training. Proceedings of International Conference on Intelligent Systems and Knowledge Engineering 2007. Advances in Intelligent Systems Research, Atlantic Press. 2007.10.2991/iske.2007.174Search in Google Scholar

[31] Yang Wang, Wei Zhang, Hongxing Li. Application of artificial fish swarm algorithm in image registration. Computer Modelling & New Technologies. 2014;18(12B):510-516.Search in Google Scholar

[32] Hasan S, Quo TS, Shamsuddin SM, Sallehuddin R. Artificial Neural Network Learning Enhancement Using Artificial Fish Swarm Algorithm. In: Proceedings of the 3rd International Conference on computing and Informatics, ICOCI 2011 (pp. 8-9).Search in Google Scholar

[33] Wang CR, Zhou CL, Ma JW. An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. In Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on 2005 Aug 18 (Vol. 5, pp. 2890-2894). IEEE.Search in Google Scholar

[34] Azad MA, Rocha AM, Fernandes EM. Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems. Swarm and Evolutionary Computation. 2014;14:66-75.10.1016/j.swevo.2013.09.002Search in Google Scholar

[35] Sathya J, Geetha K. Automtic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm. Pol J Med Phys Eng. 2017;23(2):29-36.10.1515/pjmpe-2017-0006Search in Google Scholar

[36] Yousef WA, Wagner RF, Loew MH. Assessing classifiers from two independent data sets using ROC analysis: a nonparametric approach. IEEE Trans Pattern Anal Mach Intell. 2006;28(11):1809-1817.10.1109/TPAMI.2006.21817063685Open DOISearch in Google Scholar