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.
@article{arxiv.2404.05763,
title = {Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection},
author = {Suman Sourabh and Murugappan Valliappan and Narayana Darapaneni and Anwesh R P},
journal= {arXiv preprint arXiv:2404.05763},
year = {2024}
}