An intracranial hemorrhage （ICH） is a kind of severe emergency that occurs suddenly in patients’ brain with strong symptoms and high mortality. So it is of great significance to diagnose ICH automatically and quickly based on brain CT images. However， effective clinical application requires not only the accuracy， speed and interpretation ability of models， but also especially the emphasis given to the missed detection of bleeding. Therefore， cost-sensitive Faster R-CNN is proposed in this paper to diagnose ICH， through an automatic adjustment mechanism for the proportion of training samples and a hyperparameter introduced to loss function to measure the importance of positive samples. It can pay more attention to the missed detection situations to improve the detection effect， and diagnose ICH by located target region. A network structure with optimal performance and appropriate parameter is selected for good effect of detection and diagnosis through experiments. And then， results are measured by several indexes. It is shown that the cost-sensitive Faster R-CNN model can detect bleeding well by focusing on missed checks， so as to improve the diagnosis effect under the unbalanced cost.