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CCTCOVID:使用紧凑型卷积变压器从胸部 X 光图像中检测 COVID-19。

CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers.

机构信息

Department of Computer Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Department of Cellular and Molecular Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran.

出版信息

Front Public Health. 2023 Feb 27;11:1025746. doi: 10.3389/fpubh.2023.1025746. eCollection 2023.

Abstract

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.

摘要

COVID-19 是一种攻击上呼吸道和肺部的新型病毒。其人际传播速度相当快,这给个人生活的几乎各个方面都带来了严重的问题。虽然有些感染的人可能完全没有症状,但也有许多人经常出现从轻度到重度的症状。除此之外,全球数千例死亡病例表明,在社区中检测 COVID-19 是一项紧迫的需求。实际上,这主要是借助于筛选 CT 和 X 射线等医学图像来实现的。然而,繁琐的临床程序和大量的日常病例给医务人员带来了巨大的挑战。基于深度学习的方法在广泛的医学任务中表现出了巨大的潜力。因此,我们提出了一种基于 Transformer 的方法,使用紧凑型卷积 Transformer(CCT)从 X 射线图像中自动检测 COVID-19。我们的广泛实验证明了所提出方法的有效性,准确率达到 99.22%,优于以往的工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b41/10009152/7bd1026dbd27/fpubh-11-1025746-g0001.jpg

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