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• W4281707004 endingPage "1463" @default.
• W4281707004 startingPage "1447" @default.
• W4281707004 abstract "COVID-19 has emerged as a global pandemic affecting the world, and its adverse effects on society still continue. So far, about 243.57 million people have been diagnosed with COVID-19, of which about 4.94 million have died. In this study, a new model, called COVIDetNet, is proposed for automated COVID-19 detection. A lightweight CNN architecture trained instead of the popular and pretrained convolution neural network (CNN) models such as VGG16, VGG19, AlexNet, ResNet50, ResNet100, and MobileNetV2 from scratch with chest x-ray (CXR) images was designed. A new feature set was created by concatenating the features of all layers of the designed CNN architecture. Then, the most efficient features chosen among the features concatenating with the Relief feature selection algorithm were classified using the support vector machine (SVM) method. The experimental works were carried out on a public COVID-19 CXR database. Experimental results demonstrated 99.24% accuracy, 99.60% specificity, 99.39% sensitivity, 99.04% precision, and an F1 score of 99.21%. Also, in comparison to AlexNet and VGG16 models, the deep feature extraction durations were reduced by approximately 6-fold and 38-fold, respectively. The COVIDetNet model provided a higher accuracy score than state-of-the-art models when compared to multi-class research studies. Overall, the proposed model will be beneficial for specialist medical staff to detect COVID-19 cases, as it provides faster and higher accuracy than existing CXR-based approaches." @default.
• W4281707004 created "2022-06-13" @default.
• W4281707004 creator A5042224763 @default.
• W4281707004 date "2022-06-10" @default.
• W4281707004 modified "2023-10-06" @default.
• W4281707004 title "<scp>CoviDetNet</scp>: A new <scp>COVID</scp>‐19 diagnostic system based on deep features of chest x‐ray" @default.
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• W4281707004 doi "https://doi.org/10.1002/ima.22771" @default.
• W4281707004 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35935665" @default.
• W4281707004 hasPublicationYear "2022" @default.
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