Contracting path based on a Dense block.

Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnosis or delayed diagnosis in clinical practice and affect the therapeutic effect. We propose a fully automated approach to brain tumor magnetic resonance imaging (MRI) classification, consisted by a feature extractor based on the improved U-Net and a classifier based on convolutional recurrent neural network (CRNN). The encoder of the feature extractor based on dense block, is used to enhance feature propagation and reduce the number of parameters. The decoder uses residual block to reduce the weight of some features for improving the effect of MRI spatial sequence reconstruction, and avoid gradient disappearance. Skip connections between the encoder and the decoder effectively merge low-level features and high-level features. The extract feature sequence is input into the CRNN-based classifier for final classification. We assessed the performance of our method for grading glioma, glioma isocitrate dehydrogenase1 (IDH1) mutation status classification and pituitary tumor texture classification on two datasets, glioma or pituitary tumors collected in a local affiliated hospital and glioma imaging data from TCIA. Compared with commonly models and new models, our model achieves higher accuracy, with an accuracy of 90.72%, classified glioma IDH1 mutation status with an accuracy of 94.35%, and classified pituitary tumor texture with an accuracy of 94.64%.