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Advanced Polyp Segmentation Using U-Net Architecture: A Review
Abstract
Early detection of polyps in colonoscopy images is crucial for preventing and treating colorectal cancer, a leading cause of cancer deaths worldwide. Accurate segmentation, which isolates polyps within the image, is essential for this detection process. This paper reviews the application of deep learning, specifically the U-Net architecture, for polyp segmentation. Thanks to its encoder-decoder architecture, U-Net can efficiently collect contextual information while maintaining spatial features, making it suitable for such use. Benchmark dataset evaluation confirms the effectiveness of U-Net models in accurately segmenting polyps within colonoscopy images. The presented technique is a viable option for computer-aided polyp detection, with the potential to improve early cancer diagnosis.
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