지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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.
목차
등록된 정보가 없습니다.
참고문헌 (25)
참고문헌 신청
http://www.ncc.re.kr
H. Katai / 2018 / Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001-2007) / Gastric Cancer 21(1):144~154
박현아 / 2015 / 위암 검진 권고안 / 대한의사협회지 58(5):373~384
T. Hirasawa / 2018 / Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images / Gastric Cancer (0123456789):1~8
S. Menon / 2014 / How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis / Endosc. Int. Open 02(02):E46~E50
H. Katai / 2018 / Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001-2007) / Gastric Cancer 21(1):144~154
박현아 / 2015 / 위암 검진 권고안 / 대한의사협회지 58(5):373~384
T. Hirasawa / 2018 / Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images / Gastric Cancer (0123456789):1~8
S. Menon / 2014 / How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis / Endosc. Int. Open 02(02):E46~E50
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