EAI Endorsed Transactions on e-Learning https://publications.eai.eu/index.php/el <p>EAI Endorsed Transactions on e-Learning is open access, a peer-reviewed scholarly journal focused on topics belonging to the variegated and engaging e-Learning landscape, ranging from various types of distance learning (e.g., online, mobile, cloud, hybrid) to virtual laboratory environments supported by sound pedagogies, cutting-edge technologies and much more. The journal publishes research, review, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing.</p> <p><strong>INDEXING</strong>: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on e-Learning 2032-9253 <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 4.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> Improvements in Brain Tumor Segmentation Methods Based on Convolutional Neural Networks https://publications.eai.eu/index.php/el/article/view/6080 <p>Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.</p> Yuzhuo Li Lihong Zhang Yingbo Liang Chongxin Xu Tong Liu Copyright (c) 2024 Yuzhuo Li, Lihong Zhang, Yingbo Liang, Chongxin Xu, Tong Liu https://creativecommons.org/licenses/by-nc-sa/4.0 2024-12-13 2024-12-13 11 10.4108/eetel.6080