医学分析中基于深度表征的COVID-19图像分类技术

Morarjee Kolla, H. R. Rao, N. Kumar
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引用次数: 0

摘要

Covid-19是当今一种快速发展的疾病,通过接触影响人类健康。医学界还没有找到任何可以立即使用的疫苗,一些国家最近发布了疫苗。最近,这种病毒破坏了各国的人类健康和财政状况。COVID-19疫苗研究在许多国家处于临床试验阶段。这种疾病主要影响患者的肺部。近年来,深度学习方法被广泛应用于具有大规模数据的放射图像分类中。卷积神经网络(CNN)被广泛用于胸片图像上的COVID-19肺炎分类诊断,以帮助放射科医生进行医学分析。最近,一些研究人员开发了检测这种病毒的工具,减少了胸部x光检查的时间。本文讨论了通过深度学习模型对大规模放射图像进行分类,可以帮助保护自己免受已经感染病毒的人的方法。本研究比较了各种研究方法,并对未来的研究方向提出了令人振奋的见解。©2021作者。
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COVID-19 image classification techniques in medical analysis using deep representations
Covid-19 is a fast-growing disease that affects human health with contacts nowadays. The medical community has not found any vaccine for immediate use, and some countries recently released vaccines. The human health and financial status of various countries spoiled recently with this virus. COVID-19 vaccine research is at the clinical trial stage in many countries. Mainly this disease affects the lungs of the patients. Recently deep learning approaches are widely using in radiographic image classifications with large-scale data. Convolutional Neural Networks (CNN) are widely used to diagnose COVID-19 pneumonia classification on Chest radiographic images to help radiologists in medical analysis. Recently some researchers developed tools to detect the virus, and they reduce the time of chest X-ray interpretation. This article discusses methods that can help protect themselves from those already infected with the virus by classifying the large-scale radiographic images with deep learning models. This study compares various methodologies and observes exciting insights for future research directions. © 2021 Author(s).
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