Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909163467046912 |
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| author | Sourabh, Suman Valliappan, Murugappan Darapaneni, Narayana P, Anwesh R |
| author_facet | Sourabh, Suman Valliappan, Murugappan Darapaneni, Narayana P, Anwesh R |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_05763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection Sourabh, Suman Valliappan, Murugappan Darapaneni, Narayana P, Anwesh R Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.05763 |