ArticleBioMed research international2022
Detection and Classification of Colorectal Polyp Using Deep Learning.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 44 citations in OpenAlex.
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- QRNet: A Quaternion-Based Retinex Framework for Enhanced Wireless Capsule Endoscopy Image Quality.Bioengineering (Basel, Switzerland) · 2025Article
- Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images.Scientific reports · 2025Article
- Enhancing colorectal polyp classification using gaze-based attention networks.PeerJ. Computer science · 2025Article
- Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions.Endoscopy international open · 2024Review
- Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net.Heliyon · 2024Article
- Deep learning system for true- and pseudo-invasion in colorectal polyps.Scientific reports · 2024Article
- Retracted: Detection and Classification of Colorectal Polyp Using Deep Learning.BioMed research international · 2024Article
- Article
- Comparative Analysis of Machine Learning Models for Image Detection of Colonic Polyps vs. Resected Polyps.Journal of imaging · 2023Article
- YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification.Healthcare (Basel, Switzerland) · 2023Article
- Detection of Colorectal Polyps from Colonoscopy Using Machine Learning: A Survey on Modern Techniques.Sensors (Basel, Switzerland) · 2023Review
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Review
- Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.Annals of gastroenterologyReview
Corrections and comments
- Retraction · 2024-03-20Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy.
Indexed as
Identifiers
What OpenQuestion holds
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.