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• W4220666719 endingPage "105344" @default.
• W4220666719 startingPage "105344" @default.
• W4220666719 abstract "Many countries in the world have been facing the rapid spread of COVID-19 since February 2020. There is a dire need for efficient and cheap automated diagnosis systems that can reduce the pressure on healthcare systems. Extensive research is being done on the use of image classification for the detection of COVID-19 through X-ray and CT-scan images of patients. Deep learning has been the most popular technique for image classification during the last decade. However, the performance of deep learning-based methods heavily depends on the architecture of the deep neural network. Over the last few years, metaheuristics have gained popularity for optimizing the architecture of deep neural networks. Metaheuristics have been widely used to solve different complex non-linear optimization problems due to their flexibility, simplicity, and problem independence. This paper aims to study the different image classification techniques for chest images, including the applications of metaheuristics for optimization and feature selection of deep learning and machine learning models. The motivation of this study is to focus on applications of different types of metaheuristics for COVID-19 detection and to shed some light on future challenges in COVID-19 detection from medical images. The aim is to inspire researchers to focus their research on overlooked aspects of COVID-19 detection." @default.
• W4220666719 created "2022-04-03" @default.
• W4220666719 creator A5000996281 @default.
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• W4220666719 date "2022-05-01" @default.
• W4220666719 modified "2023-09-29" @default.
• W4220666719 title "Metaheuristics based COVID-19 detection using medical images: A review" @default.
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• W4220666719 doi "https://doi.org/10.1016/j.compbiomed.2022.105344" @default.
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