Classification of breast cancer with mammogram images using various transformations and machine learning techniques

Abstract

Breast cancer is one of the leading diseases for women in the world. It is ranked second among all types of cancers, to cause death in women. The root cause of breast cancer is not known still now. So, there are no proper preventive measures for this killer disease. But the early detection is necessary to reduce the mortality rate. The early detection of breast cancer and treatment leads to an increase in the survival rate of women. Mammography is a standard radiological screening technique, which is used for early detection of breast cancer. Nowadays, mammogram is examined by newlineradiologists to find the abnormal regions. However, due to several reasons second reading is needed to get more accurate results. As a result, early detection of breast cancer is achieved through the development and usage of Computer Aided Diagnosis (CAD) system. From the previous research, it is clear that, more number of computer aided system have been developed and evaluated in order to achieve the better classification accuracy of breast cancer. Still, it is important and necessary to increase the efficiency and the classification accuracy of the CAD system for breast cancer detection. Hence, the computational framework is developed for the breast cancer diagnosis and classification, which has an improved efficiency over the other existing methods. The radiologists use this developed computational framework as the second reader without any manual interruption and coding knowledge. newline newline