Performance of Convolutional Neural Networks for Polyp Localization on Public Colonoscopy Image Datasets.

ABSTRACT: Colorectal cancer is one of the most frequent malignancies. Colonoscopy is the de facto standard for precancerous lesion detection in the colon, i.e., polyps, during screening studies or after facultative recommendation. In recent years, artificial intelligence, and especially deep learning techniques such as convolutional neural networks, have been applied to polyp detection and localization in order to develop real-time CADe systems. However, the performance of machine learning models is very sensitive to changes in the nature of the testing instances, especially when trying to reproduce results for totally different datasets to those used for model development, i.e., inter-dataset testing. Here, we report the results of testing of our previously published polyp detection model using ten

SUBMITTER: Nogueira-Rodriguez A 

PROVIDER: S-EPMC9027927 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature