article · Journal of Computational Design and Engineering
Brain imaging methods are vital for identifying the underlying causes of brain cell damage. Early detection of brain tumours can substantially enhance treatment outcomes while preventing clinical complications. Automated brain tumour segmentation from magnetic resonance imaging has emerged as an essential medical image analysis task, enabling the precise identification of tumour type, dimensions, and anatomical location. This review examines magnetic resonance imaging modalities and evaluates prevailing segmentation methodologies, placing particular emphasis on recent advances achieved through deep learning frameworks. The analysis details the foundational architectural components of convolutional neural network algorithms utilised in image segmentation tasks. Across the assessed approaches, hybrid techniques and convolutional neural network models demonstrate superior effectiveness for segmenting brain tumours from magnetic resonance scans compared to alternative strategies.
Accurate detection of brain tumours is essential for timely clinical intervention and better patient survival. By identifying the most reliable automated segmentation approaches, such as convolutional neural networks and hybrid systems, researchers and clinicians can focus on tools that improve diagnostic precision. This supports faster evaluation of magnetic resonance scans and reduces the risk of diagnostic delays in critical healthcare settings.
The review highlights automated segmentation tools that could assist clinical radiologists and healthcare software developers in diagnosing tumour characteristics from magnetic resonance imaging. Because the work is a survey of existing algorithms and algorithmic building blocks rather than a newly deployed clinical software package, the underlying technologies remain at an applied research stage requiring robust validation before real-world diagnostic deployment.
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Abstract Brain imaging techniques play an important role in determining the causes of brain cell injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge benefits in improving treatment possibilities and avoiding any potential complications that may occur to the patient. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researchers have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network (CNN). The goal of this survey is to present a brief overview of magnetic resonance imaging (MRI) modalities and discuss common methods of brain tumor segmentation from MRI images, including brain tumor segmentation using deep learning techniques, as well as the most important contributions in this field, which have shown significant improvements in recent years. Finally, we focused in summary on the building blocks of the CNN algorithms used for image segmentation. In entire survey methodology, it has been observed that hybrid techniques and CNN-based segmentation are more effective for brain tumor segmentation from MRI images.
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DOI: 10.1093/jcde/qwac141
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