ArticlePloS one2021
Gastric polyp detection in gastroscopic images using deep neural network.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Deep Learning for Pathology: YOLOv8 with EigenCAM for Reliable Colorectal Cancer Diagnostics.Bioengineering (Basel, Switzerland) · 2025Article
- Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.Medical & biological engineering & computing · 2025Article
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Evaluating the detection rate and pathological features of polyps in patients with upper gastrointestinal endoscopy.World journal of gastrointestinal endoscopy · 2025Article
- Review
- Cascade-EC Network: Recognition of Gastrointestinal Multiple Lesions Based on EfficientNet and CA_stm_Retinanet.Journal of imaging informatics in medicine · 2024Article
- Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.Scientific reports · 2024Article
- A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images.Scientific reports · 2024Article
- Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Colorectal image analysis for polyp diagnosis.Frontiers in computational neuroscience · 2024Article
- Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals.Postgraduate medical journal · 2023Review
- Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Analysis of risk factors for postoperative bleeding and polyp recurrence in adolescents with gastric polyps treated with endoscopic mucosal resection: a retrospective cohort study.Translational pediatrics · 2023Article
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5.Computational and mathematical methods in medicine · 2022Article
- Polyp Detection from Colorectum Images by Using Attentive YOLOv5.Diagnostics (Basel, Switzerland) · 2021Article
- Deep neural network approaches for detecting gastric polyps in endoscopic images.Medical & biological engineering & computing · 2021Article
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Authors and funding
6 authors.
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Abstract
This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. We propose a feature extraction and fusion module and combine it with the YOLOv3 network to form our network. This method performs better than other methods in the detection of small polyps because it can fuse the semantic information of high-level feature maps with low-level feature maps to help small polyps detection. In this work, we use a dataset of gastric polyps created by ourselves, containing 1433 training images and 508 validation images. We train and validate our network on our dataset. In comparison with other methods of polyps detection, our method has a significant improvement in precision, recall rate, F1, and F2 score. The precision, recall rate, F1 score, and F2 score of our method can achieve 91.6%, 86.2%, 88.8%, and 87.2%.
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