Brain Tumor Segmentation and Classification in Mr Images Using Residual U-Net Semantic Segmentation Model
Keywords:
Brain tumor stage classification, CNN model, Residual U-Net, Segmentation, TverskyAbstract
Brain tumor identification makes use of machine learning and computer vision methods in order to automatically identify and categorize brain cancers in medical images such as MRI scans. A model for the segmentation and identification of brain tumors based on a residual U-Net is proposed in this work. The Residual U-Net is an altered variant of the U-Net architecture that is used in semantic segmentation tasks. It incorporates the concept of residual connections, which allows for the model to learn more complex representations of the input data and can result in more accurate segmentation. Residual connections allow for deeper networks to be trained while mitigating the vanishing gradient problem. This resulted in accurate segmentation of brain tumors, particularly in cases where the tumors are small or located in complex regions of the brain. The proposed model obtained a higher tversky of 0.89 and higher PSNR of 30.
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https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation
B. Anilkumar, P. Rajesh Kumar, –Multi brain tumor classification in MR brain images through transfer learning model, Journal of Applied science and computations (JASC) 7 (May 2020) 41–49.
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