| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 105 === In this thesis, we continue to improve the system which can automatically recognize the cecum image from colonoscopy photos based on the variability of human intestinal. This system can assist doctors to check the colonoscopy photos and reduce the burden on doctors. According to the research from Health Promotion Administration, the colorectal cancer is the top one cancer on incidence rate and medical expenses in Taiwan for late 9 years. There are many reasons which cause it and it is really hard to find symptom in the early stage of colorectal cancer. Fortunately, early treatment of colorectal cancer in T is and T1 can increase the survival rate of patient effectively. In order to detect the early stage of colorectal cancer, the colonoscopy examination regularly is very important. The colonoscopy quality is closely related to the detection of early cancer. We focus on Cecal Intubation Rate (CIR), Bowel Preparation (BP. In order to evaluate CIR, doctors need to view the great amount of colonoscopy photos with concentrations and difficult medical knowledge. Therefore, we propose a cecum recognition system to help doctors to evaluate CIR automatically. The system will remove all the specular on the training images, then use data augmentation to increase the dataset largely, then the system extracts features of cecum from the images with good BP by image processing, and we use transfer learning and semi-supervised learning algorithm to recognize cecum images. Our method achieves the average accuracy rate of 84.0% and the best accuracy rate of 87.1%.
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