ArticleJournal of healthcare engineering2021
A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images.
Article in Journal of healthcare engineering, 2021. 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 It has been retracted, and should not be counted.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 5 papers, 1 of them a synthesis that pooled it.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.Singapore medical journal · 2024Article
- Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.Diagnostics (Basel, Switzerland) · 2024Article
- Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
- Retracted: A Computer-Aided Method for Digestive System Abnormality Detection in WCE Images.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wireless capsule endoscopy (WCE) is a powerful tool for the diagnosis of gastrointestinal diseases. The output of this tool is in video with a length of about eight hours, containing about 8000 frames. It is a difficult task for a physician to review all of the video frames. In this paper, a new abnormality detection system for WCE images is proposed. The proposed system has four main steps: (1) preprocessing, (2) region of interest (ROI) extraction, (3) feature extraction, and (4) classification. In ROI extraction, at first, distinct areas are highlighted and nondistinct areas are faded by using the joint normal distribution; then, distinct areas are extracted as an ROI segment by considering a threshold. The main idea is to extract abnormal areas in each frame. Therefore, it can be used to extract various lesions in WCE images. In the feature extraction step, three different types of features (color, texture, and shape) are employed. Finally, the features are classified using the support vector machine. The proposed system was tested on the Kvasir-Capsule dataset. The proposed system can detect multiple lesions from WCE frames with high accuracy.
Indexed as
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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.