Weitere Kapitel dieses Buchs durch Wischen aufrufen
Breast cancer is the second leading cause of cancer deaths in women, and it is the most common type of cancer prevalent among women. Detecting tumor using mammogram is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a tumor is present or not. In this paper, rough set theory (RST) is integrated with back-propagation network (BPN) to classify digital mammogram images. Basically, RST is used to handle more uncertain data. Mammogram images are acquired from MIAS database. Artifacts and labels are removed using vertical and horizontal sweeping method. RST has also been used to remove pectoral muscles and segmentation. Features are extracted from the segmented mammogram image using GLCM, GLDM, SRDM, NGLCM, and GLRM. Then, the features are normalized, discretized, and then reduced using RST. After that, the classification is performed using RNN. The experimental results show that the RNN performs better than BPN in terms of classification accuracy.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
J. Michaelson, S. Satija, and R. Moore, “The pattern of breast cancer screening utilization and its consequences”, vol. 94, no. 1, pp. 37–43, 2002.
Wei Pan, “Rough set theory and its application in the intelligent systems”, Proceedings of the 7th World Congress on Intelligent Control and Automation, pp. 3076–3081, 2008.
Dongbo Zhang, Yaonan Wang, “Fuzzy-rough neural network and its application to vowel recognition”, Control and Decision, vol. 21, no. 2, pp. 221–224, 2006.
Wei Wang, and Hong Mi, “The application of rough neural network in RMF model”, Proceedings of 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pp. 210–213, 2010.
Gang Wang, Chenghong Zhang, and Lihua Huang, “A study classification algorithm for data mining based on hybrid intelligent systems”, Proceedings of Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 371–375, 2008.
Chengxi Dong, Dewei Wu, and Jing He, “Decision analysis of combat effectiveness based on rough set neural network”, Proceedings of Fourth International Conference on Natural computation, pp. 227–231, 2008.
J F Peters, L Han, and S Ramanna, “Rough neural computing in signal analysis”, Computational Intelligence, vol. 17, no. 3, pp. 493–513, 2001.
Dongbo Zhang, “Integrated methods of rough sets and neural network and their applications in pattern recognition”, Hunan university, 2007.
Weidong Zhao, and Guohua Chen. “A survey for the integration of rough set theory with neural networks”, Systems engineering and electronics, vol. 24, no. 10, pp. 103–107, 2002.
R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification”, IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 610–621, 1973.
C. Velayutham, and K. Thangavel, “Unsupervised Quick Reduct Algorithm Using Rough Set Theory”, Journal of Electronic Science and Technology (JEST), vol. 9, no. 3, pp. 193–201, 2011.
C. Velayutham, and K. Thangavel, “Entropy Based Unsupervised Feature Selection in Digital Mammogram Image Using Rough Set Theory”, International Journal of Computational Biology and Drug Design, vol. 5, no. 1, pp. 16–34, 2012.
K. Thangavel, and C. Velayutham, “Unsupervised Feature Selection in Digital Mammogram Image Using Rough Set Theory”, International Journal of Bioinformatics Research and Applications, vol. 8, no. 5, pp 436–454, 2012.
- Mammogram Image Classification Using Rough Neural Network
K. T. Rajakeerthana
- Springer India
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia