ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2023
Evaluation of deep learning methods for early gastric cancer detection using gastroscopic images.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 18 citations in OpenAlex.
- A machine learning model for distinguishing gastric cancer from intestinal metaplasia in patients with psychological symptoms.Cancer causes & control : CCC · 2026Article
- Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.BMC medical imaging · 2026Article
- Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis.Discover oncology · 2025Article
- Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection.Health information science and systems · 2024Article
- Multi-dimensional dense attention network for pixel-wise segmentation of optic disc in colour fundus images.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA timely diagnosis of early gastric cancer (EGC) can greatly reduce the death rate of patients. However, the manual detection of EGC is a costly and low-accuracy task. The artificial intelligence (AI) method based on deep learning is considered as a potential method to detect EGC. AI methods have outperformed endoscopists in EGC detection, especially with the use of the different region convolutional neural network (RCNN) models recently reported. However, no studies compared the performances of different RCNN series models.
objectiveThis study aimed to compare the performances of different RCNN series models for EGC.
methodsThree typical RCNN models were used to detect gastric cancer using 3659 gastroscopic images, including 1434 images of EGC: Faster RCNN, Cascade RCNN, and Mask RCNN.
resultsThe models were evaluated in terms of specificity, accuracy, precision, recall, and AP. Fast RCNN, Cascade RCNN, and Mask RCNN had similar accuracy (0.935, 0.938, and 0.935). The specificity of Cascade RCNN was 0.946, which was slightly higher than 0.908 for Faster RCNN and 0.908 for Mask RCNN.
conclusionFaster RCNN and Mask RCNN place more emphasis on positive detection, and Cascade RCNN places more emphasis on negative detection. These methods based on deep learning were conducive to helping in early cancer diagnosis using endoscopic images.
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