Multicentered Data Based Polyp Detection Using Colonoscopy Images Using DNN

Abstract

The diagnosis and screening of colon polyps are essential for the early detection of colorectal cancer. Polyps can be identified through colonoscopies before becoming cancerous, making accurate detection and prompt intervention critical for colorectal health. A comprehensive evaluation of deep learning models using colonoscopy images and comparisons with state-of-the-art models is presented in this study. A total of 7900 still and video sequence images from the PolypGen multicenter data set were used to train cutting-edge object detection models, including YOLOv5, YOLOv7, YOLOv8, and F-RCNN + ResNet101. In terms of accuracy, precision, recall, and mAP, the YOLOv8x model achieved the best performance with an F1 score of 0.9058, accuracy of 0.949, precision of 0.863, and [email protected]. The robustness of the model was further confirmed across varying patient demographics and conditions using the external Kvasir data set. To enhance interpretability, the EigenCam explainable AI (XAI) technique was used, offering visual insights into the model's decision-making process by highlighting the most influential regions in the input images.