To improve the performance of brain tumor diagnosis, numerous automatic brain tumor diagnosis systems that use machine learning technologies have been proposed. However, most current systems ignore the structural symmetry of brain magnetic resonance imaging (MRI) images and regard brain tumor diagnosis as a simple pattern recognition task. As a result, the performance of the current systems is not ideal. To improve the performance of the brain tumor screening process, an innovative differential feature map (DFM) block is proposed to magnify tumor regions, and DFM blocks are further combined with squeeze-and-excitation (SE) blocks to form a differential feature neural network (DFNN). First, an automatic image rectification method is applied so that the symmetry axes of brain MRI images are approximately parallel to the perpendicular axis. Moreover, a DFNN is constructed to classify the brain MRI images into two categories: “abnormal” and “normal”. The experimental results show that the average accuracy of the proposed system on two databases can reach 99.2% and 98%, and the introduction of the proposed DFM block can improve the average accuracy on these two databases by 1.8% and 1.3%, respectively, which indicates that the proposed DFM block can improve the performance of the brain tumor screening process.

为了改善脑肿瘤诊断的性能，已经提出了许多使用机器学习技术的自动脑肿瘤诊断系统。但是，当前大多数系统都忽略了脑磁共振成像（MRI）图像的结构对称性，并将脑肿瘤诊断视为简单的模式识别任务。结果，当前系统的性能不是理想的。为了提高脑肿瘤筛查过程的性能，提出了一种创新的差分特征图（DFM）块以放大肿瘤区域，并将DFM块与挤压和激发（SE）块进一步结合以形成差分特征神经网络。 （DFNN）。首先，应用自动图像校正方法，以使大脑MRI图像的对称轴近似平行于垂直轴。此外，DFNN被构造为将脑部MRI图像分为两类：“异常”和“正常”。实验结果表明，该系统在两个数据库上的平均准确率可以达到99.2％和98％，引入DFM模块可以将这两个数据库的平均准确度分别提高1.8％和1.3％，表明提出的DFM阻滞剂可以改善脑肿瘤筛查过程的性能。