DETECTION OF MICROCALCIFICATION IN MAMMOGRAMS USING AI & ML TECHNIQUES
(A new modern Approach)
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One of the new modern and most efficient methods for breast cancer early detection is mammography. A new method for the detection and classification of microcalcifications is presented. It can be done in four stages: first, preprocessing stage deals with noise removal, and normalized the image. The second stage, Fuzzy c-Means clustering (FCM) is used for segmentation and pectoral muscle extraction using area calculation and finally microcalcifications detection. The third stage consists of two-dimensional discrete wavelet transforms are extracted from the detection of microcalcifications. And ...
One of the new modern and most efficient methods for breast cancer early detection is mammography. A new method for the detection and classification of microcalcifications is presented. It can be done in four stages: first, preprocessing stage deals with noise removal, and normalized the image. The second stage, Fuzzy c-Means clustering (FCM) is used for segmentation and pectoral muscle extraction using area calculation and finally microcalcifications detection. The third stage consists of two-dimensional discrete wavelet transforms are extracted from the detection of microcalcifications. And then, nine statistical features are calculated from the LL band of the wavelet transform. Finally, the extracted features are fed as input to the Artificial Neural Network and are classified into normal or abnormal (benign or malignant) images. The given classification approach is applied to a database of 322 dense mammographic images, originating from the MIAS database. The results are analyzed using MATLAB.
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