Accurate Classification of Cancer in Mammogram Images
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.
KeywordsMammogram database Cancer cell detection Benign and malignant LBP K-NN classifier
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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