Breast Cancer Detection and Classification Using Support Vector Machines and Pulse Coupled Neural Network
This article introduces a hybrid scheme that combines the advantages of pulse coupled neural networks (PCNNs) and support vector machine, in conjunction with type-II fuzzy sets and wavelet to enhance the contrast of the original images and feature extraction. An application of MRI breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. In order to enhance the contrast of the input image, identify the region of interest and detect the boundary of the breast pattern, a type-II fuzzy-based enhancement and PCNN-based segmentation were applied. Finally, wavelet-based features are extracted and normalized and a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images.
KeywordsSupport Vector Machine Magnetic Resonance Imaging Image Magnetic Resonance Imaging Breast Pulse Couple Neural Network Breast Cancer Image
This work has been supported by Cairo University, project Bio-inspired Technology in Women Breast Cancer Classification, Prediction and Visualization.
- 4.Hassanien, A.E., Soluiman, O., El-Bendary, N.: Contrast enhancement of breast MRI images based on Fuzzy Type-II. In: Proceeding of the 6th International Conference on Soft Computing Models in Industrial and Environmental Applications, Advances in Intelligent and, Soft Computing, vol. 87, pp. 77–83. Springer Berlin Heidelberg, Berlin, Heidelberg (2011)Google Scholar
- 6.Schaefer, G., Hassanien, A.E., Jiang, J.: Computational Intelligence in Medical Imaging Techniques and Applications. CRC Press, Boca Raton (2008)Google Scholar
- 9.Salama, M.A., Hassanien, A.E., Fahmy, A.A.: Feature evaluation based fuzzy C-mean classification. In: proceeding of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)–FUZZ-IEEE 2011, Taipei, Taiwan, June 27-June 30 (2011)Google Scholar
- 11.Stephane, G., Mallat, A.: Theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. II(7), 674–693 (1989)Google Scholar
- 13.Own, H.S., Hassanien, A.E.: Image registration based in multiresolution local contrast entropy in wavelet transform domain. In: IEEE 14th International Conference In Digital Signal Processing, vol. 2, pp. 889–892. 1–3 July, Greece (2002)Google Scholar