基于内容的基于堆叠自编码器的COVID-19胸部x射线和CT图像检索框架

Fatima Zahra Benyelles, Amel Sekkal, N. Settouti
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引用次数: 3

摘要

2019年底,世界见证了一场新的毁灭性疫情:冠状病毒(covid - 19),它攻击人类肺部,导致肺炎等呼吸道感染。这种疾病正在全球迅速蔓延,甚至对保健部门、经济和社会部门造成多重损害。在本文中,我们提出了一个专门用于流行病学家调查患者0感染的框架,并建立了该国不同地区的传播路径。提出了一种基于堆叠自编码器的医学图像检索系统,用于医学图像疾病特征的识别。我们的模型使用通过SARS和其他病毒或细菌肺炎疾病的不同病理图像数据库学习的相似性测量来搜索COVID胸部x射线和CT图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Content Based COVID-19 Chest X-Ray and CT Images Retrieval framework using Stacked Auto-Encoders
Late 2019, the world witnessed a new devastating outbreak: coronavirus (COVID19) that attacks the human lungs and causes respiratory infections like pneumonia pathologies. This disease is spreading rapidly in the globe, causing multiple damages, even on the healthcare sector, economic and social sectors. In this article, we present a framework dedicated to epidemiologists for the investigation of the Patient 0 infected and establish the propagation path in different areas of the country. A Content Based Medical Image Retrieval system is proposed based on the Stacked Auto-encoders for the recognition of the disease characteristics in medical images. Our model search target COVID Chest X-Ray and CT images using similarity measurements learned through an image database of different pathologies as SARS and other viral or bacterial species of pneumonia diseases.
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