ArticleSensors (Basel, Switzerland)2019
Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 172 citations in OpenAlex.
- Computer-Aided Diagnosis of Gastrointestinal Ulcer and Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test Accuracy Meta-analysis.Journal of medical Internet research · 2021Pooled it
- An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.United European gastroenterology journal · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning.Scientific reports · 2025Article
- Capsule endoscopy: Do we still need it after 24 years of clinical use?World journal of gastroenterology · 2025Article
- A review of deep learning methods for gastrointestinal diseases classification applied in computer-aided diagnosis system.Medical & biological engineering & computing · 2025Review
- Recent Advancements in Localization Technologies for Wireless Capsule Endoscopy: A Technical Review.Sensors (Basel, Switzerland) · 2025Review
- Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.Journal of imaging informatics in medicine · 2024Article
- Robotic wireless capsule endoscopy: recent advances and upcoming technologies.Nature communications · 2024Review
- Integrated image and location analysis for wound classification: a deep learning approach.Scientific reports · 2024Article
- An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning.PeerJ. Computer science · 2024Article
- Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.Biomedical engineering online · 2023Article
- Video Analysis of Small Bowel Capsule Endoscopy Using a Transformer Network.Diagnostics (Basel, Switzerland) · 2023Article
- Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.Gut and liver · 2023Article
- Applying Deep Transfer Learning to Assess the Impact of Imaging Modalities on Colon Cancer Detection.Diagnostics (Basel, Switzerland) · 2023Article
- Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy.World journal of gastroenterology · 2023Article
- Robust Ulcer Classification: Contrast and Illumination Invariant Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Learning Multi-Domain Model Provides Accurate Detection and Grading of Mucosal Ulcers in Different Capsule Endoscopy Types.Diagnostics (Basel, Switzerland) · 2022Article
- Endoscopy Artefact Detection by Deep Transfer Learning of Baseline Models.Journal of digital imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.
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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.