@article{MBCF0880E,
title = "Estimating Gastrointestinal Transition Location Using CNN-based Gastrointestinal Landmark Classifier",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2020",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2020.9.3.101",
author = "Hyeon Woong Jang/Chang Nam Lim/Ye-Suel Park/Gwang Jae Lee/Jung-Won Lee",
keywords = "Capsule Endoscopy(CE), Convolutional Neural Network(CNN), Gastrointestinal Location Tracking",
abstract = "Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds."
}