近年来，卷积神经网络( ConvNets )已经迅速成为流行的机器学习技术，特别是在医学图像的分类和分割方面。其中最常见的一种脑癌是胶质母细胞瘤，早期、准确的诊断对于治疗和生存都至关重要。本研究利用深度学习技术对MRI扫描进行了检查，以研究胶质母细胞瘤的诊断。 在这个系统综述中，使用关键词从Arxiv、IEEE、Springer、ScienceDirect和PubMed数据库获取了2010年至2022年的英文研究文献。然后，根据入选和退出标准以及研究目标，从这些文章中收集了审查所需的材料。 最终选择了77篇不同的学术文章。根据已发表文章的研究，通过包括图像采集、预处理、模型设计与执行以及模型输出评估的协同方法，发现、分类和分割了胶质母细胞瘤脑肿瘤。大多数研究使用了公开可访问的图像数据库和已经训练过的算法。绝大多数研究使用Dice系数和相似性系数来评估模型性能。 本研究的结果表明，与胶质母细胞瘤检测和分类相比，胶质母细胞瘤分割受到了研究人员的更多关注。建议在胶质母细胞瘤检测和特别是分级方面进行更多研究，以便纳入支持医学诊断的系统中。 © 2023 作者, 由 Springer-Verlag GmbH Germany 接受独家许可，是 Springer Nature 的一部分。

Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glioma, and early, accurate diagnosis is essential for both treatment and survival. In this study, MRI scans were examined utilizing deep learning techniques to examine glioma diagnosis studies.In this systematic review, keywords were used to obtain English-language studies from the Arxiv, IEEE, Springer, ScienceDirect, and PubMed databases for the years 2010-2022. The material needed for review was then collected from the articles once they had been chosen based on the entry and exit criteria and in accordance with the research's goal.Finally, 77 different academic articles were chosen. According to a study of published articles, glioma brain tumors were discovered, categorized, and segmented utilizing a coordinated approach that included image collecting, pre-processing, model design and execution, and model output evaluation. The majority of investigations have used publicly accessible photo databases and already-trained algorithms. The bulk of studies have employed Dice's classification accuracy and similarity coefficient metrics to assess model performance.The results of this study indicate that glioma segmentation has received more attention from researchers than glioma detection and classification. It is advised that more research be done in the areas of glioma detection and, particularly, grading in order to be included in systems that support medical diagnosis.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.