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Advanced Integrative Thermography in Identification of Human Elevated Temperature

机译：先进的集成热成像技术可识别人体的高温

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Thermography is a non-invasive and non-contact imaging technique used widely in the medical arena. This paper investigates the analysis of thermograms with the use of Artificial Neural Networks (ANN) and Bio-statistical methods. It is desired that through these novel approaches, accurate detection using thermography technique can be achieved. The proposed method is a multi-pronged approach comprising of Regression, Radial Basis Function Network (RBFN) and Receiver Operating Characteristics (ROC) Analysis. It is a novel and combined technique that can be used to analyze complicated and massive numerical data. This integrative technique is used to analyze temperature data extracted from febrile thermograms. Through the use of ANN and Bio-statistical methods, advances are made in thermography application with regard to achieving a higher level of consistency. This allows us to have in place a reliable contactless system for mass screening of fever subjects, which enable us to differentiate febrile from non-febrile cases in as short time as possible.
机译：热成像是一种广泛用于医疗领域的非侵入性和非接触式成像技术。本文研究了使用人工神经网络（ANN）和生物统计方法对温度记录图进行的分析。期望通过这些新颖的方法，可以实现使用热成像技术的精确检测。所提出的方法是一种多管齐下的方法，包括回归，径向基函数网络（RBFN）和接收器工作特性（ROC）分析。这是一种新颖且组合的技术，可用于分析复杂的大量数值数据。该集成技术用于分析从高热温度记录图中提取的温度数据。通过使用人工神经网络和生物统计学方法，热成像应用在实现更高一致性方面取得了进步。这使我们能够建立一个可靠的非接触式系统来对发烧的受试者进行大规模筛查，这使我们能够在尽可能短的时间内区分发热和非发热病例。

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