Memory-related vulnerabilities constitute severe threats to the security of modern software. Despite the successes of deep learningbased approaches to generic vulnerability detection, they are still limited by the underutilization of flow information and coarse granularity when applied for detecting high-level memory-related vulnerabilities, resulting in high false positive rate. In this paper, we propose MVD, a statement-level memory-related vulnerability detection approach based on flow-sensitive graph neural networks (FS-GNN). FS-GNN is employed to jointly embed both unstructured information (i.e., source code) and structured information (i.e., control- and data-flow) to capture implicit memory-related vulnerability patterns. We evaluate MVD on the dataset which contains 4,353 real-world memory-related vulnerabilities, and compare our approach with three state-of-the-art deep learning-based approaches as well as five popular static analysis-based memory detectors. The experiment results show that MVD achieves better detection accuracy, outperforming state-of-the-art approaches. Furthermore, MVD makes a great trade-off between accuracy and efficiency over baselines.