Adewole Sodiq, Yeghyayan Michelle, Hyatt Dylan, Ehsan Lubaina, Jablonski James, Copland Andrew, Syed Sana, Brown Donald
Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.
Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Proc Future Technol Conf (2020). 2021 Nov;1288:426-434. doi: 10.1007/978-3-030-63128-4_32. Epub 2020 Oct 31.
Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional with biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures: GoogLeNet, AlexNet and, ResNet.
视频胶囊内窥镜（VCE）是一项新兴技术，它能够以最小的侵入性对整个胃肠道（GI）进行检查。虽然传统的活检程序是大多数胃肠道疾病诊断的金标准，但它们受到内窥镜在消化道中推进距离的限制，并且具有侵入性。VCE使胃肠病学家能够通过可视化胃肠道的各个部位来详细研究胃肠道异常情况。它在胃肠道蠕动的推动下在胃肠道中移动时捕捉连续的实时图像。尽管VCE能够进行全面检查，但查看和分析长达八小时的图像（编译为视频）既繁琐又不具有成本效益。为了为基于VCE的胃肠道疾病诊断自动化铺平道路，检测胶囊的位置将允许进行更有针对性的分析以及在胃肠道的每个区域进行异常检测。在本文中，我们比较了四种深度卷积神经网络模型，用于从VCE图像中提取特征并检测胃肠道内的解剖部位。我们的结果表明，与其他现有技术架构：GoogLeNet、AlexNet和ResNet相比，VGG-Net具有卓越的性能，平均准确率、精确率、召回率和F1分数最高。
