Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations

TL;DR

This paper introduces a new annotated endoscopic dataset for polyp detection and classification, and evaluates eight deep learning models, demonstrating their potential to improve colorectal cancer screening.

Contribution

The creation of a comprehensive, annotated dataset for polyp detection and classification, and a comparative evaluation of state-of-the-art deep learning models.

Findings

Deep CNN models show promising results for polyp detection.

The dataset serves as a benchmark for future research.

Comparative analysis highlights strengths and weaknesses of models.

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.