Automatic Generation of Polyp Image using Depth Map for Endoscope Dataset

In recent years, opportunities for diagnosis using endoscopy aiming a less invasive treatment are increasing following the disease rate of colorectal cancer. Computer-aided diagnosis has been developed based on deep learning methodology, it aiming to improve the accuracy of diagnosis and support immature medical doctors. To satisfy the learning dataset, this paper proposes a data augmentation methodology where automatic image generation of polyp images using Pix2Pix and depth map obtained from the original image. The problem of lack of the learning dataset of polyp images can be solved by the proposed approach and the effectiveness of the generated data was confirmed by the quantitative evaluation with the improved performance of SSD (Single Shot Multibox Detector) in the experiments. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.