Covid Detection from CXR Scans using Deep Multi-layered CNN

R. Bhadra, Subhajit Kar
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引用次数: 5

Abstract

Severe Acute Respiratory Syndrome Corona virus 2 (SARS-COV-2) also known as COVID-19 has been emerged as a pandemic throughout the globe recently. Therefore, accurate diagnosis of COVID-19 is necessary to fight against this pandemic situation. In this context, chest X-ray (CXR) scans play an important role in the diagnosis of the corona virus. In this paper, an intelligent detection and classification technique of COVID-19 has been proposed to assist doctors in their diagnostic prediction. A deep multi-layered convolution neural network (CNN) has been proposed to detect COVID-19 accurately from CXR scans. The proposed methodology has experimented on a combination of multiple open source publicly available datasets. Experimental results demonstrate the efficacy of the proposed methodology in COVID-19 detection from CXR images.
基于深度多层CNN的CXR扫描检测新冠病毒
最近,严重急性呼吸系统综合征冠状病毒2 (SARS-COV-2)也被称为COVID-19,在全球范围内出现了大流行。因此,准确诊断COVID-19是抗击疫情的必要条件。在这种情况下,胸部x光扫描在冠状病毒的诊断中发挥着重要作用。本文提出了一种新型冠状病毒智能检测与分类技术,以辅助医生进行诊断预测。提出了一种深度多层卷积神经网络(CNN),用于从CXR扫描中准确检测COVID-19。所提出的方法已经在多个公开可用的开源数据集的组合上进行了实验。实验结果证明了该方法在CXR图像中检测COVID-19的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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