ArticleMultimedia tools and applications2022
Identification of gastric cancer with convolutional neural networks: a systematic review.
Article in Multimedia tools and applications, 2022. 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.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Confocal Laser Endomicroscopy: Real-Time Histology at the Fingertips: A Comprehensive Review of Current Applications of Endomicroscopy in Barrett Esophagus, Inflammatory Bowel Disease, and Colorectal Lesions.Medicina (Kaunas, Lithuania) · 2026Review
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Artificial intelligence as a predictive tool for gastric cancer: Bridging innovation, clinical translation, and ethical considerations.World journal of gastrointestinal oncology · 2025Article
- Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.Frontiers in immunology · 2025Article
- Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.Health information science and systems · 2024Article
- Gastric Cancer Image Classification: A Comparative Analysis and Feature Fusion Strategies.Journal of imaging · 2024Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- GestroNet: A Framework of Saliency Estimation and Optimal Deep Learning Features Based Gastrointestinal Diseases Detection and Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical Observation of MRI Scanning Combined with Clinical Nursing for Surgical Breast Cancer Patients.International journal of analytical chemistry · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 - 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.
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