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      Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

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          Abstract

          The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients’ records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions.

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          Most cited references35

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          Automated detection of COVID-19 cases using deep neural networks with X-ray images

          The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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            Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures

            Significance The ongoing pandemic of COVID-19 challenges globalized societies. Scientific and technological cross-fertilization yields broad availability of georeferenced epidemiological data and of modeling tools that aid decisions on emergency management. To this end, spatially explicit models of the COVID-19 epidemic that include e.g. regional individual mobilities, the progression of social distancing, and local capacity of medical infrastructure provide significant information. Data-tailored spatial resolutions that model the disease spread geography can include details of interventions at the proper geographical scale. Based on them, it is possible to quantify the effect of local containment measures (like diachronic spatial maps of averted hospitalizations) and the assessment of the spatial and temporal planning of the needs of emergency measures and medical infrastructure as a major contingency planning aid.
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              The impact of the MIT-BIH Arrhythmia Database

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                Author and article information

                Contributors
                b.majidi@khatam.ac.ir
                Journal
                New Gener Comput
                New Gener Comput
                New Generation Computing
                Ohmsha (Tokyo )
                0288-3635
                1882-7055
                27 June 2021
                : 1-24
                Affiliations
                [1 ]GRID grid.470104.6, Department of Computer Engineering, , Khatam University, ; Tehran, Iran
                [2 ]GRID grid.21100.32, ISNI 0000 0004 1936 9430, Emergency and Rapid Response Simulation (ADERSIM) Artificial Intelligence Group, Faculty of Liberal Arts and Professional Studies, , York University, ; Toronto, Canada
                [3 ]GRID grid.425174.1, ISNI 0000 0004 0521 8674, Process Management and Business Intelligence, , University of Applied Sciences Upper Austria, ; Steyr, Austria
                [4 ]GRID grid.1027.4, ISNI 0000 0004 0409 2862, Faculty of Science, Engineering and Technology, , Swinburne University of Technology, ; Melbourne, Australia
                Author information
                http://orcid.org/0000-0001-6309-6407
                Article
                131
                10.1007/s00354-021-00131-5
                8236221
                f0c6eefd-89c5-4702-9562-d3d3b9fedccd
                © Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 19 December 2020
                : 19 June 2021
                Categories
                Article

                emergency response,covid-19 pandemic,blockchain,computer aided diagnosis,machine learning

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