Abstract
Breast cancer is one of the most common cancers among women. About two out of three invasive breast cancers are found in women with age 55 or older. A Mammogram (low energy X ray of breast) done to detect breast cancer in the early stage when it is not possible feel a lump in the breast. In this paper we have proposed a method to detect microcalcifications and circumscribed masses and also classify them as Benign or malignant. The proposed method consists of three steps: The first step is to find region of interest (ROI). The second step is wavelet and texture feature extraction of ROI. The third step is classification of detected abnormality as benign or malignant using Support vector machine (SVM) classifier. The proposed method was evaluated using Mini Mammographic Image Analysis Society (Mini-MIAS) dataset. The proposed method has achieved 92% accuracy.
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Vishrutha, V., Ravishankar, M. (2015). Early Detection and Classification of Breast Cancer. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_45
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DOI: https://doi.org/10.1007/978-3-319-11933-5_45
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
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