Breast Cancer Detection and Classification Using New Fuzzy Level Set and Local Linear Radial Basis Function Neural Networ

Abstract

According to the World Health Organization, breast cancer diagnosis is the main cause of cancer death among women in the world. Breast cancer occurs generally in world especially undeveloped and developing countries mortality rates are still high, due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide distribution of the use and analysis of these images. The main propose of a method is to detect and classify mammographic injuries using the regions of attention of breast images. This research work proposes decomposing of each image using fuzzy level set rule algorithms and classification by using local linear radial basis functional neural network (LLRBFNN). Further to enhance the performance of accuracy by controlling parameters of level set evaluation are considered from the results of fuzzy clustering. This approach will combine image and shape of surface features, which can be applied for detection and classification of breast cancer diseases. The result of this research works the best methods needed to detect different classes of breast cancer such as benign or malignant (cancer).