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http://dx.doi.org/10.9718/JBER.2018.39.5.213

Evaluation of Transfer Learning in Gastroscopy Image Classification using Convolutional Neual Network  

Park, Sung Jin (Department of Biomedical Engineering, College of Medicine, Gachon University)
Kim, Young Jae (Department of Biomedical Engineering, College of Medicine, Gachon University)
Park, Dong Kyun (Department of Gastroenterology, Gil Medical Center, Gachon University College of Medicine)
Chung, Jun Won (Department of Gastroenterology, Gil Medical Center, Gachon University College of Medicine)
Kim, Kwang Gi (Department of Biomedical Engineering, College of Medicine, Gachon University)
Publication Information
Journal of Biomedical Engineering Research / v.39, no.5, 2018 , pp. 213-219 More about this Journal
Abstract
Stomach cancer is the most diagnosed cancer in Korea. When gastric cancer is detected early, the 5-year survival rate is as high as 90%. Gastroscopy is a very useful method for early diagnosis. But the false negative rate of gastric cancer in the gastroscopy was 4.6~25.8% due to the subjective judgment of the physician. Recently, the image classification performance of the image recognition field has been advanced by the convolutional neural network. Convolutional neural networks perform well when diverse and sufficient amounts of data are supported. However, medical data is not easy to access and it is difficult to gather enough high-quality data that includes expert annotations. So This paper evaluates the efficacy of transfer learning in gastroscopy classification and diagnosis. We obtained 787 endoscopic images of gastric endoscopy at Gil Medical Center, Gachon University. The number of normal images was 200, and the number of abnormal images was 587. The image size was reconstructed and normalized. In the case of the ResNet50 structure, the classification accuracy before and after applying the transfer learning was improved from 0.9 to 0.947, and the AUC was also improved from 0.94 to 0.98. In the case of the InceptionV3 structure, the classification accuracy before and after applying the transfer learning was improved from 0.862 to 0.924, and the AUC was also improved from 0.89 to 0.97. In the case of the VGG16 structure, the classification accuracy before and after applying the transfer learning was improved from 0.87 to 0.938, and the AUC was also improved from 0.89 to 0.98. The difference in the performance of the CNN model before and after transfer learning was statistically significant when confirmed by T-test (p < 0.05). As a result, transfer learning is judged to be an effective method of medical data that is difficult to collect good quality data.
Keywords
Gastroscope; Convolutional Neual Network; Transfer learning; Resnet; Inception; VGGnet;
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