Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations
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Issue Date
2021-08-17Author
Li, Kaidong
Fathan, Mohammad I.
Patel, Krushi
Zhang, Tianxiao
Zhong, Cuncong
Bansal, Ajay
Rastogi, Amit
Wang, Jean S.
Wang, Guanghui
Publisher
Public Library of Science
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
© 2021 Li et al. This work is licensed under a Creative Commons Attribution 4.0 International License.
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Show full item recordAbstract
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.
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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
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