Loading...
Thumbnail Image
Publication

Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations

Li, Kaidong
Fathan, Mohammad I.
Patel, Krushi
Zhang, Tianxiao
Zhong, Cuncong
Bansal, Ajay
Rastogi, Amit
Wang, Jean S.
Wang, Guanghui
Citations
Altmetric:
Abstract
Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.
Description
Date
2021-08-17
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
Research Projects
Organizational Units
Journal Issue
Keywords
Citation
Li K, Fathan MI, Patel K, Zhang T, Zhong C, Bansal A, et al. (2021) Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations. PLoS ONE 16(8): e0255809. https://doi.org/10.1371/journal.pone.0255809
Embedded videos