Extreme learning machine based classification for detecting micro-calcification in mammogram using multi scale features

In the human body, there are some genes that are lead to the growth of the cells.The mutation of these genes arc called cancer. Breast cancer is higher in women, and which will causes largest number of cancer related deaths among women. Breast cancer rates are higher among women in many countries. To increase the results of breast cancer and survival, early diagnosis is crucial. There arc two early screening plans for breast cancer: early detection and screening. Limited resource parameters with low health systems where most women are diagnosed in the late stages and should organize early diagnosis programs based on knowledge of the first signs and symptoms. Many methods arc used to test women to identify cancer before all symptoms appear. Mammography is one of the methods in which an image of the breast used to detect and diagnose breast cancer tumors. Micro-calcification can be found in mammogram and it will indicate the presence of breast cancer. Preprocessing, feature extraction and classification are the three important steps to detect the micro calcification in mammogram. And there are different classifiers used for the classification of micro calcification. In this paper we analyze the performance of different classifiers and find out the best one for the classification using multi scale features.