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Data Preprocessing via Compositions Multi-Channel Mri Images to Improve Brain Tumor Segmentation
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Data Preprocessing via Compositions Multi-Channel Mri Images to Improve Brain Tumor Segmentation
The magnetic resonance imaging (MRI) is the essential non- invasive diagnostics for the brain. It allows to build the de- tailed 3D image of the brain, notably including different types of soft tissues. In the paper, we compare how multi-channel data compo- sition and segmentation approach influences the model?s per- formance. Our aim consists of the binary segmentation with observing Dice and Recall Precision metrics. It is common to use 2D slices as input for the neural networks. Due to the multi-channel structure of MRI data, it means that there is a set of new ways (comparing with RGB images) how to com- bine data as input for machine learning algorithms. We eval- uate several possible combinations for multi-channel data.
The magnetic resonance imaging (MRI) is the essential non- invasive diagnostics for the brain. It allows to build the de- tailed 3D image of the brain, notably including different types of soft tissues. In the paper, we compare how multi-channel data compo- sition and segmentation approach influences the model?s per- formance. Our aim consists of the binary segmentation with observing Dice and Recall Precision metrics. It is common to use 2D slices as input for the neural networks. Due to the multi-channel structure of MRI data, it means that there is a set of new ways (comparing with RGB images) how to com- bine data as input for machine learning algorithms. We eval- uate several possible combinations for multi-channel data.
