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      Image Processing for the Prevention of Infectious Diseases : Determination of Mask Wearing, Measurement of Hand Washing Time, and Disinfection Support System

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          Abstract

          The new coronavirus infection (COVID-19) has spread to numerous countries around the world since several cases of the disease were first reported in late December 2019 in China. Currently, the WHO strongly recommends infection prevention measures such as wearing masks, hand washing, and frequent disinfection of high-touch surfaces, but there were many arguments against infection prevention policy in March 2020. For example, the WHO did not recommend the use of masks for the healthy general public. In Japan, wearing a mask was required before the habit of wearing a mask was established, which gave additional works of checking whether customer was wearing a mask to the employees. To reduce the workloads of employee and ensure mask-wearing, we started providing the mask-wearing system free of charge on March 5, 2020. We also developed hand washing time estimation and disinfection support system. It is useful to accumulate data on the status of implementation of countermeasures by our application, which leads to gain useful knowledge regarding countermeasures against COVID-19 as well as other infectious diseases. In this paper, we describe the development and introduction impacts of these systems in a pandemic emergencies. In addition, because of the security and privacy issues in running these image analysis applications, we discuss the delivery methods suitable for each service.

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          A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in the Era of the COVID-19 Pandemic

          Highlights • A hybrid deep and machine learning model proposed for face mask detection. • The model can impede the Coronavirus transmission, specially COVID-19. • Three face mask datasets have experimented with this research. • The introduced model achieves high performance in the experimental study.
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            Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

            Highlights • A novel deep learning model for medical face mask detection. • The model can help governments to prevent the COVID-19 transmission. • Two medical face mask datasets have been tested. • The YOLO-v2 with ResNet-50 model achieves high average precision.
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              Dlib-ml: A machine learning toolkit

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                Author and article information

                Contributors
                higuchi@lightblue-tech.com
                taniguchi@lightblue-tech.com
                kawasaki@lightblue-tech.com
                atom@lightblue-tech.com
                Journal
                New Gener Comput
                New Gener Comput
                New Generation Computing
                Ohmsha (Tokyo )
                0288-3635
                1882-7055
                16 September 2021
                : 1-14
                Affiliations
                [1 ]Lightblue Technology Inc., Tokyo, Japan
                [2 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, University of Tokyo, ; Tokyo, Japan
                Article
                137
                10.1007/s00354-021-00137-z
                8444173
                38437813-1682-4e28-82c9-d4a1fc531047
                © 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
                : 15 December 2020
                : 8 September 2021
                Categories
                Article

                covid-19,mask,hand washing,disinfection,image processing
                covid-19, mask, hand washing, disinfection, image processing

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