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Rapid detection of COVID-19 from chest X-ray images using deep convolutional neural networks
International Journal of Biomedical Engineering and Technology ; 41(1):42005.0, 2023.
Article in English | EMBASE | ID: covidwho-2244043
ABSTRACT
The entire world is suffering from the corona pandemic (COVID-19) since December 2019. Deep convolutional neural networks (deep CNN) can be used to develop a rapid detection system of COVID-19. Among all the existing literature, ResNet50 is showing better performance, but with three main limitations, i.e. 1) overfitting;2) computation cost;3) loss of feature information. To overcome these problems authors have proposed four different modifications on ResNet50, naming it as LightWeightResNet50. An image dataset containing chest X-ray images of coronavirus patients and normal persons is used for evaluation. Five-fold cross-validation is applied with transfer learning. Ten different performance measures (true positive, false negative, false positive, true negative, accuracy, recall, specificity, precision, F1-score and area under curve) are used for evaluation along with fold-wise performance measures comparison. The four proposed methods have an accuracy improvement of 4%, 13%, 14% and 7% respectively when compared with ResNet50.
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Full text: Available Collection: Databases of international organizations Database: EMBASE Language: English Journal: International Journal of Biomedical Engineering and Technology Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: EMBASE Language: English Journal: International Journal of Biomedical Engineering and Technology Year: 2023 Document Type: Article