An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN

TL;DR

This paper presents an efficient deep learning approach using Faster R-CNN for automatic polyp detection in endoscopic videos, aiming to improve early diagnosis of colorectal cancer.

Contribution

The study applies and evaluates Faster R-CNN for polyp detection, demonstrating its effectiveness and competitiveness compared to existing methods.

Findings

Faster R-CNN achieves high detection accuracy.

The approach outperforms several state-of-the-art methods.

It is suitable for clinical implementation.

Abstract

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays a crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a challenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.