结合Firefly算法的CNN模型自动检测covid-19

Bouzaachane Khadija
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引用次数: 0

摘要

冠状病毒已经在世界各地的许多国家传播,并夺走了许多人的生命。此外,世界卫生组织(世卫组织)已通知公共卫生官员,COVID-19已达到全球流行病状态。因此,使用胸部CT扫描进行早期诊断可以在危急情况下帮助医学专家。本研究旨在开发一种基于网络的在线检测COVID-19的服务。为了实现我们的目标,我们将卷积神经网络(CNN)模型与萤火虫算法(FA)合并。这种组合明显改善了CNN模型的性能和效率。此外,实验表明,所提出的FACNN框架使我们能够在精密度,准确度,灵敏度,F-measure,召回率和特异性(1.0%,1.0%,1.0%,1.0%,1.0%,1.0%和1.0%)方面达到高性能。此外,还开发了一个基于网络的界面,可在几秒钟内识别和识别胸片中的COVID-19。我们期待这个网络预测器能够潜在地拯救宝贵的生命,从而对社会做出积极的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic detection of covid-19 using CNN model combined with Firefly algorithm
Coronavirus has already been spread around the world, in many countries, and it has already claimed many lives. Further, the World Health Organization (WHO) has notified public health officials that COVID-19 has reached global epidemic status. Therefore, an early diagnosis using a chest CT scan can aid medical specialists in critical situations. This study aims to develop a web-based service for detecting COVID-19 online. To achieve our goal, we merged the convolutional neural network (CNN) model with the Firefly algorithm (FA). This combination ameliorate definitely the performance and efficiency of the CNN proposed model. Furthermore, the experiments revealed that the proposed FACNN framework enables us to reach high performance with regard to precision, accuracy, sensitivity, F-measure, recall and specificity (1.0%, 1.0%, 1.0%, 1.0%, 1.0% and 1.0%). In addition, a web-based interface was developed to identify and recogonize COVID-19 in chest radiographs in just few seconds. We anticipate that this web predictor will potentially save precious lives, and therefore contribute to society positively.
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