ArticleSensors (Basel, Switzerland)2021
Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.Scientific reports · 2024Article
- Spatio-Temporal Feature Transformation Based Polyp Recognition for Automatic Detection: Higher Accuracy than Novice Endoscopists in Colorectal Polyp Detection and Diagnosis.Digestive diseases and sciences · 2024Article
- Self-Adaptive Teacher-Student framework for colon polyp segmentation from unannotated private data with public annotated datasets.PloS one · 2024Article
- A Novel Computer-Aided Detection/Diagnosis System for Detection and Classification of Polyps in Colonoscopy.Diagnostics (Basel, Switzerland) · 2023Article
- A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps.Scientific reports · 2022Article
- Computer-Aided Image Enhanced Endoscopy Automated System to Boost Polyp and Adenoma Detection Accuracy.Diagnostics (Basel, Switzerland) · 2022Article
- Polyp Detection from Colorectum Images by Using Attentive YOLOv5.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.