ArticleScientific reports2024
Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.Abdominal radiology (New York) · 2026Pooled it
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Transformer-Based Feature Extraction and Optimized Deep Neural Network for Gastric Cancer Detection.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Development and external validation of a prognostic nomogram for cancer-specific survival in elderly patients with early-stage gastric cancer.Journal of gastrointestinal oncology · 2026Article
- Endo-DET: A Domain-Specific Detection Framework for Multi-Class Endoscopic Disease Detection.Journal of imaging · 2026Article
- Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence.Biomedicines · 2026Article
- Lightweight deep learning model for gastrointestinal precancerous lesion screening with attention enhancement.Frontiers in medicine · 2026Article
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Role of artificial intelligence in gastric diseases.World journal of gastroenterology · 2025Review
- Segment Anything Model for Gastric Cancer.Cancer medicine · 2025Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.Scientific reports · 2025Article
- An integrated AI-enabled system using One Class Twin Cross Learning for early gastric cancer detection.Frontiers in oncology · 2025Article
- Differential Diagnostic Value of Gastrointestinal Ultrasound Contrast Filling Method in Differentiating Gastric Tumors and Ulcers in the Elderly.International journal of general medicine · 2025Article
- [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
- Enhanced gastric cancer classification and quantification interpretable framework using digital histopathology images.Scientific reports · 2024Article
- Gastric Cancer Image Classification: A Comparative Analysis and Feature Fusion Strategies.Journal of imaging · 2024Article
- Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning.Frontiers in oncology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.