PDF(Pubmed)
Abstract：
Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries. Computer-aided diagnostic systems based on Artificial Intelligence (AI) and computer vision are an alternative to extract features from X-ray images, providing an accurate COVID-19 diagnosis. However, specialized and expensive computational resources come across as challenging. Also, it needs to be better understood how low-cost devices and smartphones can hold AI models to predict diseases timely. Even using deep learning to support image-based medical diagnosis, challenges still need to be addressed once the known techniques use centralized intelligence on high-performance servers, making it difficult to embed these models in low-cost devices. This paper sheds light on these questions by proposing the Artificial Intelligence as a Service Architecture (AIaaS), a hybrid AI support operation, both centralized and distributed, with the purpose of enabling the embedding of already-trained models on low-cost devices or smartphones. We demonstrated the suitability of our architecture through a case study of COVID-19 diagnosis using a low-cost device. Among the main findings of this paper, we point out the performance evaluation of low-cost devices to handle COVID-19 predicting tasks timely and accurately and the quantitative performance evaluation of CNN models embodiment on low-cost devices.
摘要：
2019年冠状病毒病(COVID-19)导致严重急性呼吸道综合症-冠状病毒-2(SARS-CoV-2)，并为诊断和治疗研究打开了几个挑战。胸部X射线和计算机断层扫描（CT）扫描是检测和评估COVID在疾病不同阶段对肺部造成的损害的有效且快速的替代方法。虽然CT扫描是准确的检查,由于便宜，胸部X光检查仍然有帮助，更快,较低的辐射暴露，并在低输入国家提供。基于人工智能（AI）和计算机视觉的计算机辅助诊断系统是从X射线图像中提取特征的替代方案，提供准确的COVID-19诊断。然而,专业和昂贵的计算资源给人的印象是具有挑战性的。此外，我们需要更好地理解低成本设备和智能手机如何支持人工智能模型来及时预测疾病。甚至使用深度学习来支持基于图像的医疗诊断，一旦已知技术在高性能服务器上使用集中式智能，仍然需要解决挑战，使得将这些模型嵌入到低成本设备中变得困难。本文通过提出人工智能即服务架构(AIaaS)来阐明这些问题，混合AI支持操作，集中式和分布式，目的是在低成本设备或智能手机上嵌入已经训练好的模型。通过使用低成本设备进行COVID-19诊断的案例研究，我们证明了我们的架构的适用性。在本文的主要研究结果中,我们指出了低成本设备的性能评估，以及时准确地处理COVID-19预测任务，以及CNN模型在低成本设备上的定量性能评估。
