Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations
This work addresses the need for reliable computer-aided systems in colonoscopy to improve colorectal cancer screening, but it is incremental as it focuses on dataset creation and comparative evaluations without introducing new methods.
The authors tackled the problem of colorectal cancer screening by creating a new annotated endoscopic dataset for polyp detection and classification, and they evaluated eight deep learning models, finding that deep CNN models show promise with results demonstrating their effectiveness.
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.