Classification of Esophageal Disease Images Based on Deep Fusion Network with Weight Transfer

Esophageal cancer (EC) is the sixth leading cause of cancer mortality and has one of the poorest survival rates among all cancers. Endoscopy is the usual method for diagnosing esophageal disease, but it may be subject to doctors' subjective judgments. Objective tools can assist doctors in enhancing their accuracy. This paper proposes a deep fusion network (DFY-Net) based on the weight transfer (WL) method, which uses the Kvasir public dataset, high-resolution images cropped from public video databases, and some private Barrett's esophagitis (BE) Endoscopy pictures. The FDY-Net architecture utilizes a modified ResNet50 network (named RY_RNet50) and VGG16 for feature extraction and selectively freezes specific layers during training. Its top-level part uses support vector machine (SVM) as a classifier to classify esophagitis and BE. Experimental results show that the overall classification accuracy of DFY-Net is as high as 97.71%, of which the precision of esophagitis is 97.22% and the precision of BE is 99.11%. Additionally, we use Grad-CAM (Gradient Weighted Class Activation Map) to increase the interpretability of the model. This study can provide an objective reference for endoscopists' diagnosis, help improve the diagnostic accuracy of esophageal diseases, and play an important role in the prevention of esophageal cancer.