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.

本文提出了两种深度学习模型，UNet 和 Deeplabv3，用于使用预处理的脑 MRI 图像检测和分割胶质母细胞瘤脑肿瘤，实验结果表明，Deeplabv3 在准确性上优于 UNet，但需要更多的计算资源，这些模型为胶质母细胞瘤脑肿瘤的早期检测和分割提供了一种有前途的方法。

恶性胶质母细胞瘤脑瘤的检测和分类

Detection and Classification of Glioblastoma Brain Tumor

In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scales, we design modules which employ atrous convolution in cascade
or in parallel to capture multi-scale context by adopting multiple atrous
rates. Furthermore, we propose to augment our previously proposed Atrous
Spatial Pyramid Pooling module, which probes convolutional features at multiple
scales, with image-level features encoding global context and further boost
performance. We also elaborate on implementation details and share our
experience on training our system. The proposed `DeepLabv3' system
significantly improves over our previous DeepLab versions without DenseCRF
post-processing and attains comparable performance with other state-of-art
models on the PASCAL VOC 2012 semantic image segmentation benchmark.

本文旨在设计 DeepLabv3 系统进行语义图像分割，并通过引入多尺度上下文与全局信息，使用多种空洞卷积组合实现对多尺度物体的分割，最终在 PASCAL VOC 2012 语义图像分割基准测试中取得了令人满意的表现。