Research on Application of Deep Learning in Esophageal Cancer Pathological Detection

As the “gold standard” of tumor diagnosis, pathological diagnosis is more reliable than the analytical diagnosis, ultrasound, CT, nuclear magnetic resonance, etc. Detection of esophageal cancer based on pathological slice images is focused in this paper combining with deep learning to intelligently obtain reliable detection results. A data set is built by collecting and labeling pathological slices of esophageal cancer at varying stages for model training and verification. By comparing the performance of multiple models, ResNet50 is chosen as the network model. The model is pre-trained on ImageNet with a public breast cancer data set and transferred to the task of esophageal cancer detection. The original data set is enlarged by data augmentation to improve the accuracy, effectively avoiding over-fitting. Experimental results show the test accuracy achieves 0.950 which demonstrates the feasibility of deep learning on the esophageal cancer detection with pathological slice images.