The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM
DOI:
https://doi.org/10.9781/ijimai.2017.453Keywords:
Cancer, Machine Learning, Neural Network, MedicineAbstract
Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system.Downloads
References
Amin R., “Categorizing normal and cancer patterns in mammography to detect breast cancer”, Eighth conference on vision machine and image processing, 2013.
Daroogar M., “Detection of breast cancer using an integrative approach new feature selection algorithm based on Cuckoo algorithm and support vector machine”, fifth national conference of electrical and electronic engineering of Iran, 2012.
Falahnejad M., “Mammography images segmentation using Fuzzy Nero for automatic detection of breast cancer”, second national conference on new ideas in electrical engineering, 2012.
Kazemi A., “Combination of decision trees and support vector machine for detecting breast cancer”, First national conference on intelligent systems applications in science and technology, 2013.
Abbasa, Q., Celebic, M.E. and Garcıad, I.F., “Reast mass segmentation using region-based and edge-based methods in a 4-stage multiscale system”, Biomedical Signal Processing and Control, 8(2): 204–214, March 2013.
Al-Shamlan and El-Zaart, “Feature Extraction Values for Breast Cancer Mammography Images”, Bioinformatics and Biomedical Technology (ICBBT). IEEE, Chengdu, pp. 335-340, 2010.
Dom´Ænguez, A.R. and Nandi, A.K., “Enhanced Multi-Level Thresholding Segmentation and Rank Based Region Selection for Detection of Masses in Mammograms”, Acoustics, Speech and Signal Processing. IEEE, pp. I-449–I-452, 2007.
Kimori, Y., “Morphological image processing for quantitative shape analysis of biomedical structures”, effective contrast enhancement. NCBI, 1(1): 848–853, 2013.
Li, L., Qian, W., Clarke, L.P., Clark, R.A. and Thomas, J.A., “Improving mass detection by adaptive and multiscale processing in digitized mammograms”, SPIE--The International Society for Optical Engineering. Proceedings of the SPIE, pp. 490-498, 1999.
Paramkusham, S., Kunda, M.M. Rao and Rao, B.V.V.S.N.P., “Early stage detection of breast cancer using novel image processing techniques, Matlab and Labview implementation”, Advanced Computing Technologies (ICACT). IEEE, pp. 1-5, 2013.
Rizzi, M., D’Aloia, M. and Castagnolo, B., “Computer aided detection of microcalcifications in digital mammograms adopting a wavelet decomposition”, Integrated Computer-Aided Engineering, 16(2): 91-103, 2009.
Aboul Ella Hassaniena, Hossam M. Moftahb, Ahmad Taher Azar, Mahmoud Shoman, “MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier”, Applied Soft Computing 14: 62–71, 2014.
Dheeba J., Tamil Selvi S., “A swarm optimized neural network system for classification of microcalcification in mammograms”, Journal of Medical Systems, 36(5): 3051–3061, 2012.
Dheeba J., Tamil Selvi S., “An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network”, Journal of Medical Systems, 36(5): 3223–3232, 2012.
Dheeba J., N. Albert Singh, S. Tamil Selvi, “Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network”.
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