Abstract
Early detection of polyps is one central goal of colonoscopic screening programs. To support gastroenterologists during this examination process, deep convolutional neural network can be applied for computer-assisted detection of neoplastic lesions. In this work, a Mask R-CNN architecture was applied. For training and testing, three independent colonoscopy data sets were used, including 2484 HD labelled images with polyps from our clinic, as well as two public image data sets from the MICCAI 2015 polyp detection challenge, consisting of 612 SD and 194 HD labelled images with polyps. After training the deep neural network, best results for the three test data sets were achieved in the range of recall = 0.92, precision = 0.86, F1 = 0.89 (data set A), rec = 0.86, prec = 0.80, F1 = 0.82 (data set B) and rec = 0.83, prec = 0.74, F1 = 0.79 (data set C).
© 2019 by Walter de Gruyter Berlin/Boston
This work is licensed under the Creative Commons Attribution 4.0 Public License.
