  Introduction: The present study on the development and evaluation of an
automated brain tumor segmentation technique based on deep learning using the
3D U-Net model. Objectives: The objective is to leverage state-of-the-art
convolutional neural networks (CNNs) on a large dataset of brain MRI scans for
segmentation. Methods: The proposed methodology applies pre-processing
techniques for enhanced performance and generalizability. Results: Extensive
validation on an independent dataset confirms the model's robustness and
potential for integration into clinical workflows. The study emphasizes the
importance of data pre-processing and explores various hyperparameters to
optimize the model's performance. The 3D U-Net, has given IoUs for training and
validation dataset have been 0.8181 and 0.66 respectively. Conclusion:
Ultimately, this comprehensive framework showcases the efficacy of deep
learning in automating brain tumour detection, offering valuable support in
clinical practice.
