Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.

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...

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Autores principales:	Kaidong Li, Mohammad I Fathan, Krushi Patel, Tianxiao Zhang, Cuncong Zhong, Ajay Bansal, Amit Rastogi, Jean S Wang, Guanghui Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0c845ef9b8649239febc5da999b0654
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spelling	oai:doaj.org-article:a0c845ef9b8649239febc5da999b06542021-12-02T20:17:54ZColonoscopy polyp detection and classification: Dataset creation and comparative evaluations.1932-620310.1371/journal.pone.0255809https://doaj.org/article/a0c845ef9b8649239febc5da999b06542021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255809https://doaj.org/toc/1932-6203Colorectal 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.Kaidong LiMohammad I FathanKrushi PatelTianxiao ZhangCuncong ZhongAjay BansalAmit RastogiJean S WangGuanghui WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255809 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kaidong Li Mohammad I Fathan Krushi Patel Tianxiao Zhang Cuncong Zhong Ajay Bansal Amit Rastogi Jean S Wang Guanghui Wang Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
description	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.
format	article
author	Kaidong Li Mohammad I Fathan Krushi Patel Tianxiao Zhang Cuncong Zhong Ajay Bansal Amit Rastogi Jean S Wang Guanghui Wang
author_facet	Kaidong Li Mohammad I Fathan Krushi Patel Tianxiao Zhang Cuncong Zhong Ajay Bansal Amit Rastogi Jean S Wang Guanghui Wang
author_sort	Kaidong Li
title	Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
title_short	Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
title_full	Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
title_fullStr	Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
title_full_unstemmed	Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.
title_sort	colonoscopy polyp detection and classification: dataset creation and comparative evaluations.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a0c845ef9b8649239febc5da999b0654
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_version_	1718374362875166720

Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.

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