Breast Cancer Classification with Adaptive Median Filtering, GMM Segmentation and GLCM Features
Richa Sharma, Amit Kamra, Shaffy Makkar · 2024
Breast cancer is a prominent global health issue, with early identification of abnormalities in mammogram images being pivotal for effective diagnosis and treatment. This research presents an algorithm that utilises information fusion to detect abnormalities in mammogram images and classify them as either benign or malignant. The proposed algorithm combines the adaptive median filter technique and Gaussian mixture model segmentation approach to improve detection precision. The methodology encompasses several steps: initial preprocessing of the input image to eliminate noise and enhance relevant characteristics, segmentation using GMM, followed by classification. Implementation of the algorithm employs MATLAB along with various image processing functions and techniques necessary for its objectives’ attainment. The experimental findings show that the suggested algorithm is successful at properly identifying mammography pictures and may assist in the early detection of breast cancer.