ArticleComputational and mathematical methods in medicine2021
Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Encoder-Decoder Variant Analysis for Semantic Segmentation of Gastrointestinal Tract Using UW-Madison Dataset.Bioengineering (Basel, Switzerland) · 2025Article
- Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.Current medical imaging · 2025Article
- Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images.Sensors (Basel, Switzerland) · 2024Article
- Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images.Heliyon · 2024Article
- A two-phase transfer learning framework for gastrointestinal diseases classification.PeerJ. Computer science · 2024Article
- Article
- Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.Biomedical engineering online · 2023Article
- High-Speed and Accurate Diagnosis of Gastrointestinal Disease: Learning on Endoscopy Images Using Lightweight Transformer with Local Feature Attention.Bioengineering (Basel, Switzerland) · 2023Article
- Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.Bioengineering (Basel, Switzerland) · 2023Article
- Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.Cancers · 2023Article
- A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models.Diagnostics (Basel, Switzerland) · 2023Article
- A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images.Biomedicines · 2022Article
- Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification.Sensors (Basel, Switzerland) · 2022Article
- Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen's kappa score of 0.96.
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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.