Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
This incremental work addresses brain tumor detection for clinical practice by applying an existing deep learning method to medical imaging data.
The study tackled automated brain tumor segmentation from MRI scans using a 3D U-Net model, achieving IoU scores of 0.8181 on training and 0.66 on validation datasets.
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.