Computer-aided classification of breast cancer using histopathological images can play a significant role in clinical practice by detecting the distinct type of malignant and/or benign tumor. However, currently proposed deep learning models developed using the BreakHis dataset only conduct a binary classification between benign and malignant tumors, and are also scale-dependent. This study utilizes a ResNet-50 implementation to transform images from the four magnification factors such that all images can be used for training the deep neural network. This process yields a larger training set that is also scale-independent. For this paper, we utilized a dual step approach with the first pass being binary classification and the second pass being a multi-class classifier of malignant tumors that offers higher clinical utility.