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Abstract
Abstract. This article examines multi-class brain tumor segmentation using U-
Net variants in magnetic resonance imaging. The topic is considered from the
viewpoint of medical image analysis, deep learning architecture, multimodal MRI
processing and segmentation quality assessment. Brain tumor segmentation is a
complex task because tumor subregions differ in shape, intensity, size, location and
biological meaning. U-Net and its variants remain widely used because their encoder–
decoder structure combines contextual feature extraction with spatial localization.
References
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