HKCN: A Novel Breast Cancer Identification Scheme using Homomorphic K-Means Clustering Norms
S. Padmakala, S. Revathy, M. Senthil Vadivu, T. Kamalavalli, U. Supriya, M Mathankumar · 2022
Breast cancer is the most common kind of cancer in women across the world. Furthermore, if the disease is discovered early, therapy can begin sooner and therefore be more effective. Mammograms are the most prevalent means of detecting this illness in its earliest stages. It is possible to detect micro-calcifications, which are specific to breast cancer, with the use of this approach. The goal of computer-aided detection is to help mammography in detecting breast cancer, minimizing the number of false positives and allowing for better diagnosis and treatment. Computer-aided detection systems are the product of a group of algorithms that automatically identify lesions. For this project, the major goal is to automatically enhance and segregate micro-calcifications in mammographic pictures. Homomorphic filter is one of the image enhancing techniques that has been implemented and put to use. The image classification methodology K-means segmentation also was developed and utilized. Different geometrical and spectral approach used to extract contouring and characteristics.