Detecting cerebral microbleeds can be time-consuming and prone to errors. Susceptibility weighted imaging (SWI) offers exquisite sensitivity to blood products. Furthermore, the use of SWI phase data makes it possible to differentiate diamagnetic calcifications from paramagnetic microbleeds. In this paper, we present a machine learning model based on residual neural networks, using SWI magnitude and phase data. The model was tested on 41 cases and compared with human raters with different levels of experience. A sensitivity of 93%, a positive predictive value of 80%, and 1.5 false positives per subject were achieved, outperforming both human raters and previously reported methods.