In this study, we aim to develop a multiparametric breast MRI computer-aided diagnosis (CADx) methodology using residual neural network (ResNet) deep transfer learning to incorporate information from both dynamic contrast-enhanced (DCE)-MRI and T2-weighted (T2w) MRI in the task of distinguishing between benign and malignant breast lesions. This retrospective study included 927 unique lesions from 616 women who underwent breast MR exams. A pre-trained ResNet50 was used to extract features from the maximum intensity projection (MIP) images of the second postcontrast subtraction DCE series and the center slice of the T2w series separately. Support vector machine classifiers were trained on the ResNet features to differentiate between benign and malignant lesions. The benefit of pooling features extracted from multiple levels of the network was examined on DCE MIPs. Three multiparametric methods were investigated, where information from the two sequences was integrated at the image level, feature level, or classifier level. Classification performances were evaluated with five-fold cross-validation using the area under the receiver operating characteristic curve (AUC) as the figure of merit. Using pooled features extracted from multiple layers of the ResNet statistically significantly outperformed only using features extracted from the end of the network (P = .002, 95% CI of ▵AUC: [0.007, 0.029]). The multiparametric classifiers using pooled features yielded AUCImageFusion=0.85±0.01, AUCFeatureFusion=0.87±0.01, and AUCClassifierFusion=0.86±0.01, respectively. The feature fusion method statistically significantly outperformed using DCE alone (P = .01, 95% CI of ▵AUC: [0.004, 0.022]), and all three methods statistically significantly outperformed using T2w alone (P < .001).