According to the World Health Organization (WHO) breast cancer is the major reason of death among women and its impact on women is 2.1 million per year. Only in 2018 approximately 15% (62700) of women are died due to breast cancer. To detect this breast cancer oncologist rely on two methods i.e. early diagnosis and screening. To identify cancers before any symptoms appear screening plays an important role and in screening Mammography is heart of breast cancer detection. Apart from this Clinical Breast Exams, Breast Self-Exam and many other methodologies are used. Screening for breast cancer is too long and time consuming process if approach is manual analysis and it's performed on medical images. It's also unfeasible for huge data sets. That's the reason we required self-automated, efficient and more accurate machine to identify or capture the breast cancer as minimum as possible amount of time. We found the solution of this problem is Deep Learning Method. It provides the results in short period of time as compare to other techniques and giving the better accuracy for detection of Breast cancer. In this paper we focuses on, by using which methodology we got the more accurate results and how much amount of time is required to do this process. In this project we are going to deal with different classifiers like CNN, KNN, Inception V3, SVM and ANN. By using ANN we are going to detect the Breast Cancer. We are also going to compare the results of SVM with ANN Technique.