论文标题
评估自动化机器学习服务，从X射线和CT图像中检测Covid-19
Assessing Automated Machine Learning service to detect COVID-19 from X-Ray and CT images: A Real-time Smartphone Application case study
论文作者
论文摘要
最近的SARS COV-2爆发为我们提供了一个独特的机会，可以研究非介入和可持续的AI解决方案。肺部病仍然是全球发病率和死亡率高的主要医疗挑战。主要的肺疾病是肺癌。直到最近，全世界还目睹了这场新颖的冠状病毒疫情Covid19的全球大流行。我们体验了肺和心脏的病毒感染如何在全球范围内夺走了数千人的生命。近年来，随着人工智能的前所未有的进步，机器学习可用于轻松检测和对医学图像进行分类。它比人类放射科医生更快，大多数时间更准确。实施后，它将更具成本效益和节省时间。在我们的研究中，我们评估了Microsoft认知服务检测和分类CoVID19的疗效，该COVID19诱导了基于X射线和CT图像的其他病毒/细菌性肺炎的肺炎。我们想评估基于ML的快速应用开发（RAD）环境在医学图像诊断领域的含义和准确性。这项研究将使我们能够以基于ML的诊断决策支持系统（DSS）的响应，以应对COVID19这样的大流行状况。优化后，训练有素的网络达到了96.8％的平均精度，该精度被用作消费的Web应用程序。但是，在移植到智能手机进行实时推理时，同一训练的网络与Web应用程序相同。这是我们研究的主要兴趣。作者认为，有关于此问题的进一步研究的范围。这项研究的主要目标之一是开发和评估基于AI驱动的智能手机的实时应用程序的性能。通过不可靠的互联网服务促进世界上设备齐全和人手不足的农村医疗保健中心的主要诊断服务。
The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform the same like Web Application when ported to Smartphone for Real-time inference. Which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goal of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Application. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.
