由于人体桥小脑角区的脑膜瘤与听神经瘤在影像学的表现以及发病位置极其相似，临床诊断极易发生误诊．采用深度学习方法建立肿瘤自动检测模型，能有效降低人工诊断主观性，减少误诊漏诊率，提高工作效率．而数据集的多样性及图像质量的优越性很大程度上决定了检测模型的性能．针对医学图像数据集稀缺、类别数量不平衡及成像质量较差等问题，本文提出一种改进损失函数的深度卷积生成对抗网络（Deep Convolutional Generative Adversarial Networks，DCGAN）进行脑膜瘤与听神经瘤检测模型的数据增强，并与传统数据集增强方法进行了对比．结果显示通过改进的DCGAN优化数据集后，脑膜瘤与听神经瘤检测模型的精确率、特异性以及均值平均精度值（Mean Average Precision，mAP）分别较原数据集提高了0.014 6、0.022 4、0.030 0，上升至0.932 8、0.898 6、0.930 0．实验结果表明，通过DCGAN对数据集进行优化处理后，在脑肿瘤临床检测领域中，能较好地提高其模型的检测性能，更为可靠地辅助临床医学诊断．

Due to the extreme similarity in imaging manifestations and locations of onset between meningiomas and acoustic neuromas in the CPA (cerebellopontine angle) region of the human body, clinical diagnosis is prone to misdiagnosis. Establishing an automatic tumor detection model using deep learning methods can effectively reduce the subjectivity of manual diagnosis, decrease missed diagnosis rates, and improve work efficiency. The diversity of datasets and superiority of image quality largely determine the performance of the detection model. This paper proposes a DCGAN (deep convolutional generative adversarial networks) with improved loss function for data augmentation of meningioma and acoustic neuroma detection models to address the issues of scarce medical image datasets, imbalanced number of categories, and poor imaging quality. Compared with traditional dataset augmentation methods, the results show that after optimizing the dataset with DCGAN, the accuracy, specificity, and mAP (mean average precision) of the brain tumor detection model increase by 0.014 6, 0.022 4, and 0.030 0 respectively compared to the original dataset, reaching 0.932 8, 0.898 6, and 0.930 0. The study demonstrates that optimizing datasets with DCGAN can significantly improve the performance of the brain tumor detection model, providing a more reliable tool for clinical medical diagnosis.