By the start of 2020, the novel coronavirus (COVID-19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID-19 rapidly and effectively is by analyzing chest X-ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND-CNN) architecture for the recognition of COVID-19. This network consists of a set of differently-sized hidden layers all created from scratch. The performance of this RND-CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID-19 datasets. Each of these datasets consists of medical images (X-rays) in one of three different classes: chests with COVID-19, with pneumonia, or in a normal state. The proposed RND-CNN model yields encouraging results for its accuracy in detecting COVID-19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID-19 dataset.

中文翻译：

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使用 X 射线图像识别 COVID-19 的随机初始化卷积神经网络

到 2020 年初，新型冠状病毒 (COVID-19) 已被宣布为全球大流行病，由于其传染性和严重性，几项研究都集中在对抗其持续传播上。一种快速有效地检测 COVID-19 的潜在解决方案是使用深度学习 (DL) 模型分析胸部 X 光图像。卷积神经网络 (CNN) 已被认为是用于早期诊断的特别有效的技术，但大多数仍然存在局限性。在这项研究中，我们提出了一种新颖的随机初始化 CNN (RND-CNN) 架构来识别 COVID-19。该网络由一组不同大小的隐藏层组成，这些隐藏层都是从头开始创建的。该 RND-CNN 的性能使用两个公共数据集进行评估：COVIDx 和增强型 COVID-19 数据集。这些数据集中的每一个都包含三个不同类别之一的医学图像（X 射线）：患有 COVID-19、患有肺炎或处于正常状态的胸部。所提出的 RND-CNN 模型在检测 COVID-19 结果方面的准确度令人鼓舞，在 COVIDx 数据集上实现了 94% 的准确度，在增强的 COVID-19 数据集上实现了 99% 的准确度。