Glioblastoma brain tumors are highly malignant and often require early detection and accurate segmentation for effective treatment. We are proposing two deep learning models in this paper, namely UNet and Deeplabv3, for the detection and segmentation of glioblastoma brain tumors using preprocessed brain MRI images. The performance evaluation is done for these models in terms of accuracy and computational efficiency. Our experimental results demonstrate that both UNet and Deeplabv3 models achieve accurate detection and segmentation of glioblastoma brain tumors. However, Deeplabv3 outperforms UNet in terms of accuracy, albeit at the cost of requiring more computational resources. Our proposed models offer a promising approach for the early detection and segmentation of glioblastoma brain tumors, which can aid in effective treatment strategies. Further research can focus on optimizing the computational efficiency of the Deeplabv3 model while maintaining its high accuracy for real-world clinical applications. Overall, our approach works and contributes to the field of medical image analysis and deep learning-based approaches for brain tumor detection and segmentation. Our suggested models can have a major influence on the prognosis and treatment of people with glioblastoma, a fatal form of brain cancer. It is necessary to conduct more research to examine the practical use of these models in real-life healthcare settings.

翻译：胶质母细胞瘤属高度恶性肿瘤，早期检测与精确分割对有效治疗至关重要。本文提出基于UNet和Deeplabv3两种深度学习模型，利用预处理后的脑部MRI图像实现胶质母细胞瘤的检测与分割。我们从准确率和计算效率两个维度对模型性能进行评估。实验结果表明，两种模型均能实现胶质母细胞瘤的精确检测与分割。然而，Deeplabv3在准确率方面优于UNet，但需消耗更多计算资源。本研究所提模型为胶质母细胞瘤的早期检测与分割提供了有前景的解决方案，可辅助制定有效治疗策略。未来研究可聚焦于在保持Deeplabv3高准确率的前提下优化其计算效率，以满足临床实际应用需求。总体而言，本方法有效推动了基于深度学习的医学图像分析技术在脑肿瘤检测与分割领域的发展。对于胶质母细胞瘤这类致命性脑癌患者而言，本研究所提模型将对预后判断与治疗方案产生重要影响。后续仍需进一步研究模型在实际医疗场景中的应用效果。