A Comprehensive Study For Polyp Segmentation From Colonoscopy Video Using Deep Learning Perspective

Colorectal Cancer is now one of the prominent cause of cancerous death worldwide. As its early detection and prevention are critical tasks and depends on size and shapes of polyps. From past few years, deep learning is emerging as a powerful approach for addressing this challenge. This abstract provides an overview of the deep learning perspective for polyp segmentation, highlighting key advances, methods, and their clinical efficiency. Deep learning models, especially Convolutional Neural Networks (CNNs), have revolutionized medical image analysis, including polyp segmentation. CNNs excel at learning hierarchical features directly from colonoscopy images, enabling pixelwise segmentation with high accuracy. Architectures like U-Net, Mask R-CNN, and their variants have been tailored to the specific requirements of polyp segmentation, capturing intricate details and variations in polyp appearances. In this paper, we propose a novel approach that leverages the metaphor of a “chef” to optimize polyp segmentation using a lightweight neural network. Inspired by a chef's ability to finesse ingredients into a delectable dish, our method fine-tunes a lightweight network to achieve real-time polyp segmentation accuracy.