A Comparative Study on Polyp Classification using Convolutional Neural Networks

TL;DR

This study compares various convolutional neural network models for classifying colorectal polyps, demonstrating that CNNs can achieve accuracy comparable or superior to gastroenterologists, aiding early cancer detection.

Contribution

It provides a comparative analysis of CNN models for polyp classification, highlighting their effectiveness and potential to improve diagnostic accuracy.

Findings

CNN models achieve high classification accuracy

Performance is comparable or better than gastroenterologists

Study guides future research in automated polyp detection

Abstract

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.