Detection and Classification of Glioblastoma Brain Tumor

• URL: http://arxiv.org/abs/2304.09133v1
• Date: Tue, 18 Apr 2023 17:09:16 GMT
• Title: Detection and Classification of Glioblastoma Brain Tumor
• Authors: Utkarsh Maurya, Appisetty Krishna Kalyan, Swapnil Bohidar and Dr. S. Sivakumar
• Abstract summary: We are proposing two deep learning models, namely UNet and Deeplabv3, for the detection and segmentation of glioblastoma brain tumors. Our experimental results demonstrate that both UNet and Deeplabv3 models achieve accurate detection and segmentation of glioblastoma brain tumors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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