# Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches

arXiv: https://arxiv.org/abs/1906.11463 · HTML: https://mlsanity.com/paper/1906.11463/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 16 of 16)

- [Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge](https://mlsanity.com/paper/2202.12031/) (2022)
- [Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay](https://mlsanity.com/paper/2308.14100/) (2023)
- [COTR: Convolution in Transformer Network for End to End Polyp Detection](https://mlsanity.com/paper/2105.10925/) (2021)
- [Early Detection and Localization of Pancreatic Cancer by Label-Free Tumor Synthesis](https://mlsanity.com/paper/2308.03008/) (2023)
- [BlazeNeo: Blazing fast polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2203.00129/) (2022)
- [GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification](https://mlsanity.com/paper/2205.02840/) (2022)
- [SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis](https://mlsanity.com/paper/2306.00197/) (2023)
- [AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation](https://mlsanity.com/paper/2105.00402/) (2021)
- [Simple U-net Based Synthetic Polyp Image Generation: Polyp to Negative and Negative to Polyp](https://mlsanity.com/paper/2302.09835/) (2023)
- [Real-time automatic polyp detection in colonoscopy using feature enhancement module and spatiotemporal similarity correlation unit](https://mlsanity.com/paper/2201.10079/) (2022)
- [InvNorm: Domain Generalization for Object Detection in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2205.02842/) (2022)
- [Knowledge distillation with a class-aware loss for endoscopic disease detection](https://mlsanity.com/paper/2207.09530/) (2022)
- [Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive Learning](https://mlsanity.com/paper/2307.07603/) (2023)
- [A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n](https://mlsanity.com/paper/2507.10864/) (2025)
- [Colorectal Polyp Detection in Real-world Scenario: Design and Experiment Study](https://mlsanity.com/paper/2101.04034/) (2021)
- [A multi-center analysis of deep learning methods for video polyp detection and segmentation](https://mlsanity.com/paper/2603.04288/) (2026)

## Abstract (arXiv)

Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.