Mammogram Image to Detect Breast Cancer Using K-Means Clustering Algorithm

Abstract

Breast cancer is the uncontrolled proliferation of a group of cells in the breast and is the second leading cause of death for women in the world. The disease can be cured if detected in the early stages. A lot of research has been done to correctly detect the tumor, but a 100% accurate method has not been found. Research on breast cancer detection using digital image processing is not new, but many new approaches are being considered in this field to accurately predict the tumor area. The current approach consists of detecting the tumor area visually and also finding out in which area the tumor is most concentrated. CC and MLO dualview mammographic screening images are widely used in the diagnostic process. This project presents a method for detecting a tumor area and classifying a normal and oncological patient. Preprocessing operations are performed on the input mammographic image and unwanted parts are removed from the image. Tumor regions are segmented from the image using a morphological operation and are highlighted on the original mammographic image. If the image on the mammogram is normal, it means that the patient is healthy. This work mainly focuses on finding the best algorithms for detecting tumors present in the breast. A number of algorithms were used in the proposed work, but the most suitable for cancer detection is the combination of K-Means clustering algorithm. K-Means classification accuracy is 95% accurate output will be predicted. Keywords: Image Processing, Breast Cancer, K-Means clustering, Dilation, Closing, Edge Detection, Mammography screening images