Abstract
Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many non-invasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective.
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Mohanty, A.K., Champati, P.K., Senapati, M.R., Lena, S.K. (2012). Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions. In: Patnaik, S., Yang, YM. (eds) Soft Computing Techniques in Vision Science. Studies in Computational Intelligence, vol 395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25507-6_3
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DOI: https://doi.org/10.1007/978-3-642-25507-6_3
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