Gliomas are characterized by high morbidity and high mortality in primary tumors. The identification of glioma type is helpful for radiologists to facilitate correct medical judgments and better prognosis for patients. In order to avoid harm to patients caused by a biopsy, radiologists attempt to classify Magnetic Resonance Images(MRI) using deep learning methods. In the present paper, we propose a deep learning convolutional neural network ResNet based on the pyramid dilated convolution for Gliomas classification. The pyramid dilated convolution is integrated into the bottom of Resnet to increase the receptive field of the original network and improve the classification accuracy. After adding the pyramid dilated convolution model, the receptive field of the original network underlying convolution was improved. A clinical dataset is used to test the pyramid dilated convolution ResNet neural network model proposed in this paper. The experimental results demonstrate that the proposed method can effectively improve glioma classification performance.

神经胶质瘤的特征在于原发性肿瘤的高发病率和高死亡率。胶质瘤类型的识别有助于放射科医生促进正确的医学判断和更好的患者预后。为了避免活检对患者造成伤害，放射科医生尝试使用深度学习方法对磁共振图像（MRI）进行分类。在本文中，我们提出了一种基于金字塔扩张卷积的深度学习卷积神经网络ResNet用于神经胶质瘤分类。金字塔扩张的卷积被集成到Resnet的底部，以增加原始网络的接收场并提高分类精度。添加了金字塔扩张的卷积模型后，改进了原始网络在卷积基础上的接受范围。临床数据集用于测试本文提出的金字塔膨胀卷积ResNet神经网络模型。实验结果表明，该方法可以有效提高神经胶质瘤的分类性能。