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Application of deep learning models in the pathological classification and staging of esophageal cancer: A focus on
Wei Wei1, Xiao-Lei Zhang2, Hong-Zhen Wang2
1Department of Oncology, Dongying People's Hospital, Dongying 257091, Shandong Province, China. ww19810122@163.com.
Background:
Esophageal 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.
Aim:
To 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.
Methods:
We 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.
Results:
The 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.
Conclusion:
Deep 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.
