ArticleDiagnostics (Basel, Switzerland)2023
Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Swarm Intelligence-Guided Hybrid Transfer Learning for Gastrointestinal Polyp Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Optimised Deep Learning for Gastrointestinal Polyp Classification: A Controlled Benchmark of Five CNN and Transformer Architectures with Grad-CAM Interpretability.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal machine learning for staging laparoscopy: a combined image analysis and morphologic tool for the discrimination of peritoneal metastasis.International journal of surgery (London, England) · 2026Article
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- Bridging the gap: Computer-aided detection and Yamada classification system matches expert performance.World journal of gastroenterology · 2025Article
- Machine Learning-Based Prediction of Histopathological Classification in Colorectal Polyps.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Detecting early gastrointestinal polyps in histology and endoscopy images using deep learning.Frontiers in artificial intelligence · 2025Article
- Use of artificial intelligence to measure colorectal polyp size without a reference object.Endoscopy international open · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study aimed to develop an AI-based system for the detection and classification of polyps using colonoscopy images. A total of about 256,220 colonoscopy images from 5000 colorectal cancer patients were collected and processed. We used the CNN model for polyp detection and the EfficientNet-b0 model for polyp classification. Data were partitioned into training, validation and testing sets, with a 70%, 15% and 15% ratio, respectively. After the model was trained/validated/tested, to evaluate its performance rigorously, we conducted a further external validation using both prospective (
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