Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.
Detecting COVID-19 patients based on fuzzy inference engine and Deep Neural Network.
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根据模糊推理引擎和深神网络检测199例患者。
DOI：
10.1016/j.asoc.2020.106906
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发表时间：
2021-03
影响因子：
8.7
通讯作者：
Abo-Elsoud MA
中科院分区：
文献类型：
--
作者：
Shaban WM;Rabie AH;Saleh AI;Abo-Elsoud MA
新冠肺炎作为一种传染病震惊了世界，仍然威胁着数十亿人的生命。最近，冠状病毒(新冠肺炎)的检测是摆在医生面前的一项关键任务。不幸的是，新冠肺炎在人与人之间传播得如此之快，在几个月内就在全球范围内接近数百万人。迅速准确地识别感染者，以便采取措施防止传播，这是非常必要的。虽然已经使用了几种医学测试来检测某些损伤，但有望达到的检测效率还没有达到。介绍了一种新的混合诊断策略(HDS)。HDS依赖于一种新的技术来对选定的特征进行排序，方法是将它们投影到建议的患者空间(PS)中。构造了一个特征连通图(FCG)，它既表示每个特征的权重，又表示与其他特征的绑定度。特征的等级是基于两个因素来确定的，第一个是特征权重，第二个是它在PS中与相邻特征的约束度。然后，利用排序的特征得到分类模型，该模型可以对新的人进行分类，以判断他们是否感染了病毒。该分类模型是由模糊推理机和深度神经网络(DNN)两个分类器组成的混合模型。建议的HDS已经与最新的技术进行了比较。实验结果表明，HDS在准确率、准确率、召回率和F度量的平均值上都优于其他竞争对手，分别为97.658%、96.756%、96.55%和96.615%。此外，HDS提供的误差值最低，为2.342%。此外，使用Wilcoxon符号等级检验和Friedman检验对结果进行了统计验证。一种新的检测新冠肺炎患者的混合诊断策略。HDS依赖于模糊逻辑和深度神经网络。排序特征被用来导出所提出的分类模型。通过10次交叉验证对所提出的策略进行了验证。对新冠肺炎患者的检测准确率为97.658%。
COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Recently, the detection of coronavirus (COVID-19) is a critical task for the medical practitioner. Unfortunately, COVID-19 spreads so quickly between people and approaches millions of people worldwide in few months. It is very much essential to quickly and accurately identify the infected people so that prevention of spread can be taken. Although several medical tests have been used to detect certain injuries, the hopefully detection efficiency has not been accomplished yet. In this paper, a new Hybrid Diagnose Strategy (HDS) has been introduced. HDS relies on a novel technique for ranking selected features by projecting them into a proposed Patient Space (PS). A Feature Connectivity Graph (FCG) is constructed which indicates both the weight of each feature as well as the binding degree to other features. The rank of a feature is determined based on two factors; the first is the feature weight, while the second is its binding degree to its neighbors in PS. Then, the ranked features are used to derive the classification model that can classify new persons to decide whether they are infected or not. The classification model is a hybrid model that consists of two classifiers; fuzzy inference engine and Deep Neural Network (DNN). The proposed HDS has been compared against recent techniques. Experimental results have shown that the proposed HDS outperforms the other competitors in terms of the average value of accuracy, precision, recall, and F-measure in which it provides about of 97.658%, 96.756%, 96.55%, and 96.615% respectively. Additionally, HDS provides the lowest error value of 2.342%. Further, the results were validated statistically using Wilcoxon Signed Rank Test and Friedman Test. A new Hybrid Diagnose Strategy (HDS) to detect COVID-19 patients. HDS relies on fuzzy logic and deep neural network. Ranked features are used to derive the proposed classification model. The proposed strategy has been validate using 10-fold cross validation. An accuracy of 97.658% for COVID-19 patients detection.
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