Colorectal Polyp Segmentation In Colonoscopy Image Using Mask RCNN

Abstract

The incidence rate of colorectal cancer is high worldwide, but the survival rate is greatly improved by early identication of polyps. Colonoscopy is the gold stan dard procedure for diagnosis and removal of colorectal lesions with the potential to evolve into cancer. Computer-aided detection systems can help gastroenterologists find polyps more accurately, which is one of the key signs of a good colonoscopy and a predictor of the likelihood of developing colorectal cancer. The size, shape, and texture of the colorectal polyp is challenging factor for computer aided detection systems. Mask RCNN is one of computer aided detection and segmentation sys tem to alleviate these challenges. In this study, we use Mask R-CNN for colorectal polyp segmentation in colonoscopy images. We used a large dataset and optimized the existing Mask RCNN model to improve its performance. We have used publicly available datasets for training 3,946 images and for testing 444 images. The trained model achieved a mean average precision of 95.75%, mean average recall of 96% and f1 score of 95.87%. Thus, optimizing the hyperparameter of Mask R-CNN us ing a large colonoscopy image dataset improves its polyp segmentation performance in colonoscopy images. The proposed method improves the early diagnosis of col orectal polyps and helps to save lives