ArticleWorld journal of gastroenterology2025
Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on Wave-Vision Transformer.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- AI-driven pathology in esophageal cancer: from early screening to precision prognostics.Frontiers in oncology · 2026Review
- Deep learning models for pathological classification and staging of oesophageal cancer.World journal of clinical oncology · 2025Article
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Authors and funding
7 authors.
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Abstract
backgroundEsophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can improve patient survival rates. The progression of esophageal cancer follows a sequence from esophagitis to non-dysplastic Barrett's esophagus, dysplastic Barrett's esophagus, and eventually esophageal adenocarcinoma (EAC). This study explored the application of deep learning technology in the precise diagnosis of pathological classification and staging of EAC to enhance diagnostic accuracy and efficiency.
aimTo explore the application of deep learning models, particularly Wave-Vision Transformer (Wave-ViT), in the pathological classification and staging of esophageal cancer to enhance diagnostic accuracy and efficiency.
methodsWe applied several deep learning models, including multi-layer perceptron, residual network, transformer, and Wave-ViT, to a dataset of clinically validated esophageal pathology images. The models were trained to identify pathological features and assist in the classification and staging of different stages of esophageal cancer. The models were compared based on accuracy, computational complexity, and efficiency.
resultsThe Wave-ViT model demonstrated the highest accuracy at 88.97%, surpassing the transformer (87.65%), residual network (85.44%), and multi-layer perceptron (81.17%). Additionally, Wave-ViT exhibited low computational complexity with significantly reduced parameter size, making it highly efficient for real-time clinical applications.
conclusionDeep learning technology, particularly the Frequency-Domain Transformer model, shows promise in improving the precision of pathological classification and staging of EAC. The application of the Frequency-Domain Transformer model enhances the automation of the diagnostic process and may support early detection and treatment of EAC. Future research may further explore the potential of this model in broader medical image analysis applications, particularly in the field of precision medicine.
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