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Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
arXiv - CS - Human-Computer Interaction Pub Date : 2020-11-26 , DOI: arxiv-2011.13194
Morteza Hosseini, Haoran Ren, Hasib-Al Rashid, Arnab Neelim Mazumder, Bharat Prakash, Tinoosh Mohsenin

Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.

中文翻译:

使用听觉和人口统计学信息进行肺疾病诊断的神经网络

肺部疾病影响着全球和每年数百万的生命。最近爆​​发的COVID-19大流行(一种新型的肺部感染)比以往任何时候都更引起了研究界对机器辅助呼吸道诊断的关注。因此,本文致力于利用机器学习对呼​​吸系统问题进行分类,并提出了一个框架,该框架采用了尽可能多的相关信息(本工作中的听觉和人口统计学信息),因为数据集可以提高诊断系统的敏感性和特异性。首先,我们使用深度卷积神经网络(DCNN)对公开发布的肺听觉数据集进行处理和分类,然后我们利用数据集中现有的人口统计信息,并证明,结合听觉信息与人口统计信息一起进行训练时,肺分类的准确性提高了5%。由于可以使用计算机视觉提取人口统计数据,因此我们建议使用另一个并行DCNN来估计处理计算机视觉下的被测对象的人口统计学信息。最后,为了使医疗保健系统触手可及,我们测量了听觉DCNN模型在NVIDIA TX2开发板的处理组件上的部署特性。我们建议使用另一种并行DCNN来估计处理计算机所想象的被测对象的人口统计信息。最后,为了使医疗保健系统触手可及,我们测量了听觉DCNN模型在NVIDIA TX2开发板的处理组件上的部署特性。我们建议使用另一种并行DCNN来估计处理计算机所想象的被测对象的人口统计信息。最后,为了使医疗保健系统触手可及,我们测量了听觉DCNN模型在NVIDIA TX2开发板的处理组件上的部署特性。
更新日期:2020-12-01
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