Breast cancer (BC) is one of the most dangerous diseases for women. Breast screening is a technique performed to discover BC at an early stage and reduce the mortality rate. Mammography, which allows patients to identify changes in their breasts before they feel them, is the primary screening tool for BC diagnosis. In this study, pretrained convolutional neural networks (CNNs) like visual geometry group (VGG) VGG-16 and VGG-19 are implemented to detect and classify breast tumors on the INbreast dataset. In the proposed model, breast images are initially preprocessed to improve image quality and reduce computation time. Then, the parameters learned in the networks are transferred to learn with the breast parameters to improve the classification results. Therefore, this work utilized to make an efficient manipulation for the obtained information from the large volume of data generated so that that correct classification may enhance the treatment options. Furthermore, in the evaluation stage, four metrics accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were considered to measure the performance of the proposed model. It was found that the proposed model obtained accuracy, sensitivity, specificity, and AUC values of 97.1%, 96.3%, 97.9%, and 0.988%, respectively.