Mammogram Image Classification Using Rough Neural Network
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 creat
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Abstract 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.
Keywords Mammogram BPN Discretization Rough neural network (RNN) Rough set theory
K. T. Rajakeerthana Department of Electrical and Electronics Engineering, Kongu Engineering College Perundurai, Erode, Tamil Nadu 638052, India e-mail: [email protected] C. Velayutham (&) Department of Computer Science, Aditanar College of Arts and Science, Virapandianpatnam, Tiruchendur, Tamil Nadu 628216, India e-mail: [email protected] K. Thangavel Department of Computer Science, Periyar University, Salem, Tamil Nadu 636011, India e-mail: [email protected]
G. S. S. Krishnan et al. (eds.), Computational Intelligence, Cyber Security and Computational Models, Advances in Intelligent Systems and Computing 246, DOI: 10.1007/978-81-322-1680-3_15, Springer India 2014
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1 Introduction Breast cancer is the most commonly diagnosed cancer prevalent among women. It is the second leading cause of cancer death in women, exceeded only by lung cancer. The chance that breast cancer may be responsible for a woman’s death is about 1 in 36 (about 3 %). In the United Kingdom, every year there are about 45,000 cases diagnosed, and more than 13,200 women die from this cancer, i.e., death rate is 29.3 % (Cancer Research UK, 2009). About 1 in 8 (12 %) women in United States will develop breast cancer during their lifetime. Mammography is the most common investigation technique used by radiologists in the screening and in the diagnosis of breast cancer. Mammography is a low-dose X-ray procedure that allows visualization of the internal structure of the breast. Mammography is highly accurate, but like most medical tests, it is not perfect. On an average, mammography will detect about 80–90 % of the breast cancers in women without symptoms [1]. Many researchers implemented the rough neural network (RNN) in different domains. Wei Pan [2] have used neural network in intelligent control. Dongbo Zhang and Yaonan Wang [3] have applied fuzzy RNN in vowel recognition. Wei Wang and Hong
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