# A Comparative Study on Polyp Classification using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2007.06071 · HTML: https://mlsanity.com/paper/2007.06071/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation](https://mlsanity.com/paper/2105.00999/) (2021)
- [A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild](https://mlsanity.com/paper/2105.07625/) (2021)
- [Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks](https://mlsanity.com/paper/2102.05582/) (2021)
- [Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing](https://mlsanity.com/paper/2211.04573/) (2022)
- [Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks](https://mlsanity.com/paper/2304.13192/) (2023)
- [Gender, Smoking History and Age Prediction from Laryngeal Images](https://mlsanity.com/paper/2305.16661/) (2023)
- [Multi-Layer Dense Attention Decoder for Polyp Segmentation](https://mlsanity.com/paper/2403.18180/) (2024)
- [Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks](https://mlsanity.com/paper/2211.06814/) (2022)
- [RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research](https://mlsanity.com/paper/2511.06769/) (2025)
- [DFE-IANet: A Method for Polyp Image Classification Based on Dual-domain Feature Extraction and Interaction Attention](https://mlsanity.com/paper/2407.20843/) (2024)
- [TreeNet: Layered Decision Ensembles](https://mlsanity.com/paper/2510.09654/) (2025)

## Abstract (arXiv)

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.