基于全连接卷积神经网络的X射线图像预测Covid - 19

Sanghamita Bhoumik, Sayantan Chatterjee, Ankur Sarkar, Abhishek Kumar, Ferdin Joe John Joseph
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引用次数: 6

摘要

COVID - 19大流行使整个世界陷入瘫痪,没有任何歧视。有效的人员检测对控制感染起着至关重要的作用。通常,基于胸部x线图像的诊断采用人工方法,不仅耗时,而且为无症状患者更快地传播病毒铺平了道路。为了解决这一问题,本文提出了使用全连接卷积神经网络(CNN)对胸部x线图像进行分析。采用两种卷积变体的全连接CNN,特别是DSC,已经证明了其检测COVID - 19感染的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covid 19 Prediction from X Ray Images Using Fully Connected Convolutional Neural Network
COVID 19 pandemic has paralyzed the whole world irrespective of any discrimination. To contain the infection effective testing of people plays a vital role. Usually, chest X-ray image-based diagnosis using manual methods is carried out, which is not only time-consuming but also paves way for asymptomatic patients to transmit the virus at a faster pace. Chest X-ray image analysis using a fully connected convolutional neural network (CNN) has been proposed in this paper to solve the purpose. The fully connected CNN with two variants of convolution especially DSC has proved its efficiency in detecting COVID 19 infections.
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