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Breast cancer is the most common type of cancer worldwide in the last five years. Breast cancer is the second most common cause of death in women, after skin cancer. Breast cancer detection at an early stage has the potential to reduce morbidity and mortality. There are many technologies and diagnostic tests available to diagnose breast cancer. Artificial neural networks can automatically extract multiple features and make prediction about breast cancer. Multiple labeled images are required to train neural networks that are an unconventional method for some types of data images, such as breast magnetic resonance imaging (MRI). The most effective solution is to fine-tuning the neural network. In this paper, the proposed transfer learning model uses AlexNet in a convolutional neural network to extract range features from breast cancer MRI images in order to train the model. The proposed model is more efficient …