TITLE: Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations 

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.
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.
Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
http://arxiv.org/abs/2104.10824v2