Early Detection of Breast Cancer using Soft Computing

Breast cancer is the most harmful of all types of cancers that leads to the death of women. The initial screening test for breast cancer is made using Mammography. Masses, Calcification and Architectural distortion are the three major signs notable as causes for cancer recognized using mammogram images. Soft Computing methods provide solutions to biologically inspired problem of medical domain like breast cancer. Neural Networks, Fuzzy Logic and Genetic Algorithms contribute novel algorithms to deal with breast cancer. Breast cancer can be diagnosed using soft computing methods. The effective diagnosis of breast cancer can be achieved by using feature reduction and classification methods. The feature reduction method applied is Principal Component Analysis (PCA) and the classification method includes Support Vector Machines (SVM). The objective of this study is to improve the breast cancer early detection with the application of soft-computing techniques.