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Advancements in machine learning algorithms for brain tumor detection and segmentation: A comprehensive analysis
- 1 Snowden International School
- 2 British School of Beijing
* Author to whom correspondence should be addressed.
Abstract
Brain tumors pose a substantial health challenge globally. Their accurate detection and segmentation are crucial for effective treatment, and recent advancements in machine learning (ML) present a promising solution to these tasks. This paper provides a comprehensive analysis of traditional and modern ML algorithms for brain tumor detection and segmentation. It highlights the pivotal role of ML in advancing brain tumor analysis and how it can potentially mitigate the impact of malignant tumors. Traditional image processing techniques have shown their value but face limitations in dealing with the complexity of brain tumors. The integration of ML has substantially enhanced the capabilities of traditional detection techniques, with architectures such as convolutional neural networks (CNNs) providing improved results. Moreover, brain tumor segmentation techniques have also seen significant enhancements, with the transition from conventional techniques like Region Growing and Watershed methods to state-of-the-art deep learning methods, such as U-Net. Despite these advancements, great challenges remain. Ongoing researches are necessary to further harness the potential of ML in brain tumor diagnosis and treatment. The findings of this review underscore the significance of ML in brain tumor analysis and its profound potential impact on patient outcomes and the overall landscape of cancer treatment.
Keywords
machine learning, artificial intelligence, brain tumor analysis, brain tumor detection, brain tumor segmentation
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Cite this article
Wu,Y.;Zhang,B. (2023). Advancements in machine learning algorithms for brain tumor detection and segmentation: A comprehensive analysis. Applied and Computational Engineering,21,78-85.
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