基于卷积神经网络的海洋捕食者COVID-19检测算法

Khelili Mohamed Akram, Slatnia Sihem, K. Okba
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引用次数: 3

摘要

COVID-19因其迅速传播而成为世界上最危险的疾病之一,为研究人员早期发现它提出了新的挑战。在过去的几个月里,由于病毒的突变,在南非、印度和英国发现了新的covid - 19病毒株。由于世界卫生形势严峻,而且由于缺乏有效的治愈疫苗,病例数量不断增加,因此需要及时隔离和医疗,以及可靠地识别COVID-19,以预防和控制这场大流行。放射学图像和人工智能技术是计算机辅助医学诊断新冠病毒检测中使用最多的技术。本文提出了一种基于卷积神经网络的新型元启发式技术——海洋捕食者算法,用于检测Covid-19并很好地区分Covid-19和肺炎疾病。我们提出的系统在分类方面取得了良好的结果,准确率为93%,精密度为95%,召回率为97%,f1得分为95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convolution Neural network based Marine Predator Algorithm for COVID-19 detection
COVID-19 is among the dangerous illness in the world due to its quickly spreading, posing a new challenge for researchers to discover it early. In last few months, new covid19 virus strains have been found in South Africa, India, and United Kingdom (UK) due to the mutation of the virus. Owing this critical situation of the world health and with increased number of the cases with the absence of efficient a cure vaccine, timely quarantine and medical treatment, as well as reliable identification of COVID-19, are required to prevent and contain this pandemic. Radiology images and Artificial Intelligence techniques are the most used techniques in computer-aided medical diagnosis for Covid-19 detection. The present paper shows a Convolution Neural Network based novel metaheuristic techniques called Marine Predator Algorithm for detecting Covid-19 and well differentiate between Covid-19 and Pneumonia disease. Our proposed system achieves good results in term of classification such as 93% of accuracy, 95% of precision, 97% of recall and F1-score 95%.
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