Abstract
In the last decade, machine learning plays a vital role in the detection of breast cancer. Mammography is a proficient tool for early stage detection of breast cancer. In this work, a simple technique for breast cancer image classification in l mammogram images is proposed. Highly discriminant local binary patterns are extracted from the wavelet normalized mammogram images. K-nearest neighbor classifier is used to categorize the abnormal cancer cell images. A mammogram database is created to evaluate the efficacy of our algorithm. From the experimental results, the performance of our algorithms is comparatively good with very less computational time.
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Ethical Approval
The mammogram database used in this paper is provided by Pixel scans, Trichy. The ethical committee of Pixel scans has reviewed and approved to conduct research using this mammogram database and publish papers based on the results using that biomedical images.
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Parisa Beham, M., Tamilselvi, R., Mansoor Roomi, S.M., Nagaraj, A. (2019). Accurate Classification of Cancer in Mammogram Images. In: Saini, H., Singh, R., Kumar, G., Rather, G., Santhi, K. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-13-3765-9_8
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DOI: https://doi.org/10.1007/978-981-13-3765-9_8
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