Breast Cancer (BC) is a primary cause of cancer death among women in which breast cells expand out of control. By urging patients to seek treatment as soon as possible, the chances of survival are increased. To save the patient’s life, a new deep learning (DL) model based on the transfer learning (TL) technique is proposed for automatic detection and classification of the suspicious breast area. In the presented model, learning parameters from pre-trained models VGG-19, VGG-16, and Inception-V3 networks are transferred to improve the performance of malignant lesions classification. The key objectives of the research are to employ segmentation to automatically locate the impacted breast tumor region, reduce training time, and enhance classification accuracy. The Mammographic Image Analysis Society (MIAS) dataset is used to extract breast tumor features in the described model. For analyzing the performance of the provided model, we have used three evaluation metrics: accuracy, sensitivity, and specificity. The trials revealed that transferring parameters from the VGG-16 model is more powerful than VGG-19 and Inception V3 for BC classification, with overall specificity, accuracy, and sensitivity of 97.12%, 97.80%, and 96.43%, respectively.