Assessing Automated Machine Learning service to detect COVID-19 from X-Ray and CT images: A Real-time Smartphone Application case study

• URL: http://arxiv.org/abs/2010.02715v1
• Date: Sat, 3 Oct 2020 23:18:05 GMT
• Title: Assessing Automated Machine Learning service to detect COVID-19 from X-Ray and CT images: A Real-time Smartphone Application case study
• Authors: Razib Mustafiz, Khaled Mohsin
• Abstract summary: The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. This study will better equip us to respond with an ML-based diagnostic Decision Support System for a Pandemic situation like COVID19. One of the main goal of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Application.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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