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首页> 外文会议>International conference on artificial neural networks >Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-Based Neuromorphic Platforms
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Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-Based Neuromorphic Platforms

机译：基于人工神经网络的COPD诊断模式识别及其在基于硬件的神经形态平台上的潜在集成

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摘要

This paper presents the development of an Artificial Neural Network (ANN) model for pattern recognition for Chronic Obstructive Pulmonary Disease (COPD) diagnosis. Recent advancements in healthcare devices and the availability of numerous medical data have facilitated the management of chronic diseases. In addition, machine learning tools have made the management and diagnostic of a chronic illness more efficient by converting collected medical data from biosensors into meaningful clinical information. However, securing sensitive medical data, collected from patients, is still a challenging task. Hardware-based neural networks address this data safety concern by on-chip processing of acquired data, without cloud communications. Therefore, the presented ANN model was designed to comply with the intrinsic structure of neuromorphic platforms for future integrations.
机译：本文介绍了用于模式识别的慢性阻塞性肺疾病（COPD）诊断的人工神经网络（ANN）模型的开发。医疗保健设备的最新发展以及众多医疗数据的可用性促进了慢性疾病的管理。另外，机器学习工具通过将收集自生物传感器的医学数据转换为有意义的临床信息，使慢性病的管理和诊断更加有效。但是，保护从患者那里收集的敏感医学数据仍然是一项艰巨的任务。基于硬件的神经网络通过对获取的数据进行片上处理来解决此数据安全问题，而无需进行云通信。因此，提出的ANN模型被设计为符合神经形态平台的内在结构，以便将来集成。

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