Review and Evaluation of existing deep learning methods for Brain Tumor Detection
DOI:
https://doi.org/10.48047/z9gth477Keywords:
Transformers, Medical Image Analysis, Deep Learning, Brain Tumor, Magnetic Resonance Imaging, BraTS, Machine LearningAbstract
When it comes to medical image processing, segmenting brain tumors is an essential task. Patients' chances of survival are increased and treatment options are improved when brain tumors are detected early. It requires a great deal of time and work to manually separate brain tumors from the many MRI pictures obtained during clinical routines in order to diagnose malignancy. Automatic brain tumor segmentation of images is required. This study summarizes methods for segmenting brain tumors using magnetic resonance imaging (MRI). Automatic segmentation using deep learning approaches has been increasingly popular as of late.
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References
Christ, J. et al. (2014) “Segmentation of brain tumors using meta heuristic algorithms,” Open journal of communications and software, 2014(1), pp. 1–10. doi: 10.15764/cs.2014.01001.
Kleihues, P., Burger, P. C. and Scheithauer, B. W. (1993) “The new WHO classification of brain tumors,” Brain pathology (Zurich, Switzerland), 3(3), pp. 255–268. doi: 10.1111/j.1750-3639.1993.tb00752.x.
Mittal, M. et al. (2019) “Deep learning based enhanced tumor segmentation approach for MR brain images,” Applied soft computing, 78, pp. 346–354. doi: 10.1016/j.asoc.2019.02.036.
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