Abstract: Breast cancer is one of the common types of cancer which is affecting health of women population in the world from last few decades. Breast cancer treatments always depend upon early detection, personalized approach and knowledge of disease. From last decade there are many deep learning and machine learning algorithm are implemented by many researchers but accuracy and precision not up to the mark hence mammographic breast density classification is done subjectively by radiologist.

In this research article implementation of machine learning algorithm is proposed for mammographic breast density classification. In this approach input images are preprocessed with help of morphological operations; pectoral muscle is removed by Hough transform and Canny Edge detection techniques are used.

The images are segmented with the help of Gaussian mixture model and features are extracted using GLCM feature extraction method and then SVM classification is performed on the images. With certain modification this algorithm is suitable for clinical practice.

Keywords: Breast Cancer, BI-RADS Classification, Preprocessing, Segmentation, Feature Extraction, Mammographic Breast Density.

PDF | DOI: 10.17148/IJARCCE.2023.12810

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