Polyp Segmentation using Generative Adversarial Network.

Colorectal cancer is one of the highest causes of cancer-related death and the patient's survival rate depends on the stage at which polyps are detected. Polyp segmentation is a challenging research task due to variations in the size and shape of polyps leading to necessitate robust approaches for diagnosis. This paper studies the deep generative convolutional framework for the task of polyp segmentation. Here, the analysis of polyp segmentation has been explored with the pix2pix conditional generative adversarial network. On CVC- Clinic dataset, the proposed network achieves Jaccard index of 81.27% and Dice index of 88.48%.