Pemantic Segmentation in Medical Imaging using U-Net Convolutional Neural Networks
Keywords:
U-Net, Semantic Segmentation, Mask R-CNN, Medical Imaging, Privacy-Preserving Federated Learning, Computational Efficiency, IoU, Dice Coefficient.Abstract
This research investigates the application of U-Net convolutional neural systems in the semantic division for restorative imaging, centering on brain tumor distinguishing proof and kidney tumor division. Four cutting-edge calculations, specifically U-Net, DeepLabv3, Mask R-CNN, and LinkNet, were comprehensively assessed. Through thorough experimentation on assorted therapeutic imaging datasets, the study uncovers that Veil R-CNN shows superior division exactness, accomplishing an amazing IoU of 91.3% and a Dice coefficient of 94.2%. Comparative investigations with related work illustrate the competitiveness of the proposed approach in comparison to state-of-the-art strategies. In addition, the investigation digs into computational productivity contemplations, generalization over modalities, and factual importance testing, advertising a comprehensive appraisal of algorithmic choices. Patterns such as privacy-preserving combined learning and the integration of worldly data in dynamic imaging modalities were also investigated. The results of this investigation not as it were contribute to the headways in the semantic division for therapeutic imaging but also give practical experiences for the improvement of precise and productive apparatuses with real-world clinical applications.
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