  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.
