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• W3200312946 endingPage "e694" @default.
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• W3200312946 abstract "The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world." @default.
• W3200312946 created "2021-09-27" @default.
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• W3200312946 date "2021-09-20" @default.
• W3200312946 modified "2023-10-06" @default.
• W3200312946 title "KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN" @default.
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• W3200312946 doi "https://doi.org/10.7717/peerj-cs.694" @default.
• W3200312946 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8459788" @default.
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