Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
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Abstract (arXiv)
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.
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References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
- 2020nnU-Net for Brain Tumor Segmentationcited by 103
- 2021Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT imagescited by 5
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