ArticleDiagnostics (Basel, Switzerland)2023
Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Transformer-Based Feature Extraction and Optimized Deep Neural Network for Gastric Cancer Detection.Journal of imaging informatics in medicine · 2026Article
- An interpretable deep learning framework for intestinal metaplasia detection in gastric histopathology images.Frontiers in oncology · 2026Article
- Efficient and explainable histopathology for cancer detection using dual-teacher distillation and integrated gradients.Frontiers in medicine · 2026Article
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
- Generalized deep learning for histopathology image classification using supervised contrastive learning.Journal of advanced research · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Advanced deep learning for multi-class colorectal cancer histopathology: integrating transfer learning and ensemble methods.Quantitative imaging in medicine and surgery · 2025Article
- Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis.Frontiers in immunology · 2025Article
- Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.Diagnostics (Basel, Switzerland) · 2024Article
- Gastric Cancer Image Classification: A Comparative Analysis and Feature Fusion Strategies.Journal of imaging · 2024Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning.Frontiers in oncology · 2024Article
- Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images.Scientific reports · 2023Article
- Non-annotated renal histopathological image analysis with deep ensemble learning.Quantitative imaging in medicine and surgery · 2023Article
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7 authors.
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Abstract
Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates.
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