ArticleMedical & biological engineering & computing2021
Deep neural network approaches for detecting gastric polyps in endoscopic images.
Article in Medical & biological engineering & computing, 2021. 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, 21 citations in OpenAlex.
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- [Research progress on endoscopic image diagnosis of gastric tumors based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Review
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
- Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Efficient Synchronous Real-Time CADe for Multicategory Lesions in Gastroscopy by Using Multiclass Detection Model.BioMed research international · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.
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