ArticleBiomedicines2023
A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification Based on Ensemble XcepNet23 and ResNet18 Features.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Gastrointestinal tract disease classification from wireless capsule endoscopy images based on deep learning information fusion and Newton Raphson controlled marine predator algorithm.Scientific reports · 2025Article
- EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.Scientific reports · 2025Article
- Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks.Frontiers in medicine · 2025Article
- Optimized deep learning model for diagnosing tonsil and adenoid hypertrophy through X-rays.Frontiers in oncology · 2025Article
- Enhancing surgical decision-making in NEC with ResNet18: a deep learning approach to predict the need for surgery through x-ray image analysis.Frontiers in pediatrics · 2024Article
- Controversies Regarding Mesh Utilisation and the Attitude towards the Appendix in Amyand's Hernia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
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6 authors.
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Abstract
Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.
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