Localisation of Colorectal Polyps by Convolutional Neural Network Features Learnt from White Light and Narrow Band Endoscopic Images of Multiple Databases
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AbstractAlgorithms for localising colorectal polyps have been studied extensively; however, they were often trained and tested using the same database. In this study, we present a new application of a unified and real-time object detector based on You-Only-Look-Once (YOLO) convolutional neural network (CNN) for localizing polyps with bounding boxes in endoscopic images. The model was first pre-trained with non-medical images and then fine-tuned with colonoscopic images from three different databases, including an image set we collected from 106 patients using narrow-band (NB) imaging endoscopy. YOLO was tested on 196 white light (WL) images of an independent public database. YOLO achieved a precision of 79.3% and sensitivity of 68.3% with time efficiency of 0.06 sec/frame in the localization task when trained by augmented images from multiple WL databases. In conclusion, YOLO has great potential to be used to assist endoscopists in localising colorectal polyps during endoscopy. CNN features of WL and NB endoscopic images are different and should be considered separately. A large-scale database that covers different scenarios, imaging modalities and scales is lacking but crucial in order to bring this research into reality.
Acceptance Date18/07/2018
All Author(s) ListYali Zheng, Ruikia Zhang, Ruoxi Yu, Yuqi Jiang, Tony WC Mak, Sunny H Wong, James YW Lau, Carmen CY Poon
Journal nameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume Number2018
Article number8513337
Pages4142 - 4145
LanguagesEnglish-United Kingdom

Last updated on 2021-22-09 at 01:35