Abstract
Abstract: Wireless capsule endoscopy is an important and ongoing diagnostic procedure. It brings a lot of images throughout the journey to the patient's digestive tract and often requires automatic analysis. One of the most notable abnormalities in bleeding and spontaneous isolation of hemorrhage is an interesting research topic. We have developed a computer-based framework that utilizes the latest advances in artificial intelligence based on in-depth reading comprehension to successfully support the entire screening process from video transmission to automatic wound detection to reporting. More precisely, our method of handling multiple video uploads at the same time, automatically scans them for the purpose of identifying important video frames with potential lesions (subsequent analysis by endoscopies) and brings doctors to the methods of linking detected and previously detected lesions. or with current photographs and scientific information from relevant documents to obtain a more accurate final conclusion. Auto DL is considered useful for the site development of advanced read-through learning models. An untested endoscopic with a certain level of expertise can at least benefit from AutoDL support. Keywords: convolutional neural network, deep learning, automated deep learning, endoscopy, gastric neoplasms, neural network, deep learning model, artificial intelligence
Published Version
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