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Deep learning for automated medical image segmentation in brain tumor detection
1 Department of Computer Science, Depaul University, Chicago, USA.
2 COMSATS University Islamabad, Abbottabad Campus, Pakistan.
3 University of Engineering and Technology (UET), Lahore, Pakistan.
4 Department of Computer Science, Depaul University, Chicago, USA.
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 4377-4388.
Article DOI: 10.30574/ijsra.2024.13.2.2570
Publication history:
Received on 14 November 2024; revised on 22 December 2024; accepted on 24 December 2024
Abstract:
With the deep learning techniques making strides into medical imaging, that would be the brain tumor automated segmentation and detection success story that these techniques had. Identification and outlining of the tumor at a stage and with such accuracy as usually required in clinical practice treatment planning and hence, patient outcomes become dependent on accurate diagnosis. The new convolutional neural networks that are of cutting-edge performance like the U-Net and its variations provide strong solutions to challenging features of heterogeneous tumor structures with differing image qualities in magnetic resonance imaging. This paper presents an integrated approach harnessing deep learning techniques to segment brain tumors from complicated imaging datasets very efficiently. This involves advanced preprocessing methods coupled with new network architectures and rigorous performance evaluation metrics such as the Dice Coefficient and Jaccard Index, which offer significant segmentation accuracy improvements over traditional means of manual or semi-automated processing.
Also, the research shows how there can be a collaborative effort between computation scientists and clinicians. When combined with good datasets such as those from BraTS's repository, this approach works on addressing the obvious issues of data imbalance, overfitting, and model interpretability. The ongoing study also looks into ways federated learning and transformer-based models can be utilized to improve segmentation performance while ensuring data privacy and scalability regarding clinical settings. With all this, the automation of medical image segmentation via deep learning not only streamlines diagnosis but also allows personalized treatment strategies through early intervention. The data results from this study assert the extent to which deep learning will impact the future outcome of brain tumor detection in contributing towards precision medicine and better health delivery. As a whole, our detailed analysis enlightens about the role deep learning is going to play in reforming brain tumor segmentation for further innovative clinical practices, which will ensure more reliable, swift, and personalized patient care. This future is bright and promises much convergence between technology and medicine in early diagnosis and effective therapeutic interventions. This vibrant new field continually inspires ground-breaking and novel research.
Keywords:
Deep Learning; Automated Medical Image Segmentation; Brain Tumor Detection; Medical Imaging, U-Net; Convolutional Neural Networks (CNNs); MRI; Dice Coefficient; Data Preprocessing; Federated Learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
