Image Recognition of Esophageal Cancer based on ResNet and Transfer Learning

10.6919/ICJE.202205_8(5).0111

Qiying Ling；Xiaofang Liu

Transfer Learning ； Esophageal Cancer ； Resnet

International Core Journal of Engineering

8卷5期（2022 / 05 / 01）

863 - 869

英文

Based on the image recognition accuracy and other indicators, the feasibility of transfer learning to solve the problem of the small amount of esophageal cancer data sets and incomplete labeling information is discussed. Select 200 esophageal endoscopic images of pathological patients with esophageal cancer and 100 esophageal endoscopic images of unaffected patients. After mixing and scrambled, they are divided according to a certain proportion, and the transferred ResNet network is used for training and learning. The results show that The feasibility of applying transfer learning to esophageal cancer image recognition with a small amount of data is also demonstrated, and the feasibility of deep learning as an auxiliary diagnosis of esophageal cancer images is also demonstrated.

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