ArticleBioengineering (Basel, Switzerland)2023
GIT-Net: An Ensemble Deep Learning-Based GI Tract Classification of Endoscopic Images.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.Scientific reports · 2026Article
- An Attention-Enhanced ViT-HLNN Hybrid Ensemble Framework for Multi-Class Gastrointestinal Disease Classification.Scientific reports · 2026Article
- An EfficientNet-based hierarchical dual-encoder framework for multi-scale gastrointestinal disease detection.Scientific reports · 2026Article
- Real-Time Endoscopic Video Enhancement via Degradation Representation Estimation and Propagation.Journal of imaging · 2026Article
- EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- Robust Autism Spectrum Disorder Screening Based on Facial Images (For Disability Diagnosis): A Domain-Adaptive Deep Ensemble Approach.Diagnostics (Basel, Switzerland) · 2025Article
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- Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.BMC medical imaging · 2024Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.Diagnostics (Basel, Switzerland) · 2024Article
- 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
- Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis.PeerJ. Computer science · 2024Article
- Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT).Bioengineering (Basel, Switzerland) · 2023Article
- Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
5 authors.
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Abstract
This paper presents an ensemble of pre-trained models for the accurate classification of endoscopic images associated with Gastrointestinal (GI) diseases and illnesses. In this paper, we propose a weighted average ensemble model called GIT-NET to classify GI-tract diseases. We evaluated the model on a KVASIR v2 dataset with eight classes. When individual models are used for classification, they are often prone to misclassification since they may not be able to learn the characteristics of all the classes adequately. This is due to the fact that each model may learn the characteristics of specific classes more efficiently than the other classes. We propose an ensemble model that leverages the predictions of three pre-trained models, DenseNet201, InceptionV3, and ResNet50 with accuracies of 94.54%, 88.38%, and 90.58%, respectively. The predictions of the base learners are combined using two methods: model averaging and weighted averaging. The performances of the models are evaluated, and the model averaging ensemble has an accuracy of 92.96% whereas the weighted average ensemble has an accuracy of 95.00%. The weighted average ensemble outperforms the model average ensemble and all individual models. The results from the evaluation demonstrate that utilizing an ensemble of base learners can successfully classify features that were incorrectly learned by individual base learners.
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