Abstract: Glioma is a highly lethal primary intracranial malignant tumor. Preoperative non-invasive diagnosis is of great significance for the treatment and prognosis of glioma. In this paper， we propose a brain glioma segmentation and classification network assisted by object detection techniques. We proposed a brain glioma segmentation and classification network assisted by target detection technology. Based on multi-modal three-dimensional MRI， glioma regions were segmented and classified by WHO classification（Ⅱ/Ⅲ/Ⅳ）， IDH mutation status classification and 1p19q deletion status classification. The object detection technology was employed to obtain tumor region location information for auxiliary segmentation and classification， and modules such as SPP and FPN were also used to improve model performance. The model was trained on 664 glioma cases in the EGD data set. Finally， in the test set， the Dice score of glioma segmentation reached 0.88， and the classification accuracy of WHO classification， IDH mutation status and 1p19q deletion status reached 0.80， 0.72 and 0.90， respectively. Comparative experiments with the PSNet， ResNet50， and Unet+ResNet50 models demonstrate the effectiveness of our proposed model. At the same time， the ablation experiments of object detection module， SPP module and FPN module were carried out to verify the effect of the introduced module. The experimental results show that the model proposed in this paper can effectively perform the multi-task diagnosis of brain glioma before operation， and it is helpful for the treatment and prognosis of brain glioma.

Key words: deep learning, classify and segment networks, object detection, glioma diagnosis, MRI