Two different convolutional neural network architectures were applied to differentiate subtype breast cancer based on 5 DCE-MRI time frame images: (1) a conventional serial convolutional neural network; (2) a convolutional long short term memory (CLSTM) Network. In addition, a logistic classifier was trained using morphology and texture features, selected using a random forest algorithm. For CNN, a bounding box based on the automated tumor segmentation was used to create a cropped image of the tumor as network input. A total of 94 cancers were analyzed, including 14 triple negative, 29 HER2-positive, and 51 Hormonal-positive, HER2-negative. Upon 10-fold validation, the differentiation accuracy is 0.81-0.86 using serial CNN, and 0.88-0.95 using the CLSTM.