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International Journal of Advanced Technology and Engineering Exploration ; 9(90):623-643, 2022.
Article in English | ProQuest Central | ID: covidwho-1964885

ABSTRACT

A rapid diagnostic system is a primary role in the healthcare system exclusively during a pandemic situation to control contagious diseases like coronavirus disease-2019 (COVID-19). Many countries remain lacking to spot COVID cases by the reverse transcription-polymerase chain reaction (RT-PCR) test. On this stretch, deep learning algorithms have been strengthened the medical image processing system to analyze the infection, categorization, and further diagnosis. It is motivated to discover the alternate way to identify the disease using existing medical implications. Hence, this review narrated the character and attainment of deep learning algorithms at each juncture from origin to COVID-19. This literature highlights the importance of deep learning and further focused the medical image processing research on handling the data of magnetic resonance imaging (MRI), computed tomography (CT) scan, and electromagnetic radiation (X-ray) images. Additionally, this systematic review tabulates the popular deep learning networks with operational parameters, peer-reviewed research with their outcomes, popular nets, and prevalent datasets, and highlighted the facts to stimulate future research. The consequence of this literature ascertains convolutional neural network-based deep learning approaches work better in the medical image processing system, and especially it is very supportive of sorting out the COVID-19 complications.

2.
1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021 ; 107:8275-8287, 2022.
Article in English | Scopus | ID: covidwho-1874821

ABSTRACT

The Face Mask Detection model is used to make sure a person is wearing Mask or not. This model results from the grappling situation presented by COVID-19, resulting in the mandatory use of Masks at public places. Security agencies need to plant actual personnel to make sure all the people in public are wearing 'Masks', this model will lessen the risk of people being contacted by COVID-19. This research helps us understand a broader perspective about the Face Detection models by comparing different state of the art models. The model uses MobileNetV2 architecture, that has inverted bottlenecks and depth-wise convolutions, to filter features. The complete model is built in 2 phases, the first one consisting of making a Face mask detection model trained to detect the Face and mask, and then placing it in the Real Time environment by using the OpenCV for actually predicting the usage of Face Mask. © The Electrochemical Society

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