Researchers are motivated to investigate deep learning (DL) techniques for mammogram images because of the limitations of conventional systems-based computer-aided detection (CAD) for mammography, the extreme significance of early breast cancer identification, and the high impact of false diagnosis on patients. This study develops a model for breast cancer detection from mammogram images that employ Convolutional Neural Network (CNN) based Transfer Learning (TL). The developed structure is comprised of several stages: breast region extraction is performed to extract the Region of Interest (ROI) from the background and artifacts, a Gaussian filter is employed for noise reduction, and data augmentation is carried out to increase the size of the original images for better learning of CNN. Then, a pre-trained model-based CNN using an augmented dataset is presented. Deep features are extracted from CNN and they are trained based on using TL. A mini-MIAS dataset was conducted as the testing ground for this experiment, and the best accuracy achieved was 95.71%. The developed framework performs significantly better than other current methods when compared to them.