Cancer is the second leading reason of death worldwide. Breast cancer (BC) is the most typically diagnosed and dominant explanation of cancer-related death in middle-aged women. Invasive ductal carcinoma (IDC) is the major diagnosed type of carcinoma. Pathological analysis of the biopsy means taking the sample from any part of body (some cells or fluids) and sent to a laboratory for the test observed under the microscope. Early detection and treatment can improve endurance rates worldwide. In this paper, we propose two methods for classifying BC. In the first method, whole slide images (WSI) [1] are input into a Residual neural network (ResNet) for feature extraction. Second, features are extracted and then fed into Support Vector Machines (SVM) for BC classification. We used the open Breast Histopathology Image (BHI) Dataset where we achieved 87.80% accuracy and an 88% F-1 score.