A colonial serrated polyp classification model using white light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart

Abstract

Abstract In this study, we implemented an artificial intelligence (AI) model—Convolutional Neural Network (CNN)—to help physicians classify colonic polyps into traditional adenoma (TA), sessile serrated adenoma (SSA), and hyperplastic polyp. We collected ordinary endoscopy images under both white and NBI lights. Under white light, we collected 257 images of hyperplastic polyp (HP), 423 images of SSA, and 60 images of TA. Under NBI light, were collected 238 images of HP, 284 images of SSA, and 71 images of TA. We implemented the artificial intelligence model to build a classification model for the types of colon polyps. Our final AI classification model is built only with white light images. Our classification prediction accuracy of colon polyp type is 94%, and the discriminability of the model (area under the curve) was 98%. Thus, we can conclude that our model can effectively help physicians distinguish between TA, SSA, and HPs, and correctly identify serrated-type colon polyps.