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Chronic Wound Tissue Characterization under Telemedicine Framework

机译:远程医疗框架下的慢性伤口组织特征

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Chronic wound (CW) diagnosis is more demanding to monitor the healing process of the wound. However, the availability of specialist medical help in remote/rural areas in developing countries, like India, is a challenge. Further, visiting specially hospitals in city is both expensive and time consuming. This paper discusses the comprehensive CW diagnostic approach using three important modules, namely, wounds data acquisition (WDA), tele-wound technology network (TWTN), and wound screening and diagnostic (WSD) respectively. We have proposed a CW characterization and diagnosis under telemedicine framework to classify the tissue depending on percentage of wound and based on color variation at regular time intervals. The Bayesian classifier based wound characterization (BWC) method is proposed to identify the percentage of tissue with high accuracy. It has been observed that the BWC method provides overall accuracy of 87.11%.
机译:慢性伤口(CW)诊断更令人要求监测伤口的愈合过程。然而,像印度一样,发展中国家的远程/农村地区专业医疗帮助的可用性是一项挑战。此外,在城市的特殊医院访问既昂贵又耗时。本文讨论了使用三个重要模块的全面CW诊断方法,即伤口数据采集(WDA),远程缠绕技术网络(TWTN)和伤口筛选和诊断(WSD)。我们提出了远程医疗框架下的CW表征和诊断,以根据伤口的百分比和基于常规时间间隔的颜色变化来分类组织。基于贝叶斯分类器的伤口表征(BWC)方法是提出以高精度识别组织的百分比。已经观察到BWC方法提供87.11%的整体准确性。

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