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• W4317039276 abstract "X-ray is the most accessible imaging modality for detecting Covid-19 infection. However, X-ray image resolution depends on the amount of radiation dose. The Lesser the dosage, the lower the resolution, the higher the noise and patient safety. Detecting Covid-19 infection would be more precise with high-resolution chest X-ray images. The current article explores an edge-preserving single-scale residual learning-based super-resolution method to enhance low-resolution chest X-ray images. We used unsharp masking to preserve small, medium, and high-scale details while super-resolving the given image. The method produces a clear view of the pulmonary opacities in chest X-ray images after super-resolution reconstruction. Statistical feature metrics of first and second-order showed superior quality reconstruction by the proposed method for the given Covid-19 chest X-ray images. Further, to measure the effectiveness of super-resolution, we used an Inception v3 based deep learning model to classify chest X-ray images of Covid-19, pneumonia, and normal class. The performance of the classification model with super-resolved chest X-ray images was tested against 400 images belonging to two different classes at a time. We obtained increased precision of 94% and 96% accuracy in detecting Covid-19 infection in chest X-ray images after super-resolution compared to 64% precision and 68% accuracy before super-resolution." @default.
• W4317039276 created "2023-01-18" @default.
• W4317039276 creator A5039912315 @default.
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• W4317039276 date "2023-01-17" @default.
• W4317039276 modified "2023-09-27" @default.
• W4317039276 title "A Super-resolution-based Approach for the Detection of Covid—19 Infection From Chest X-ray Images" @default.
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• W4317039276 doi "https://doi.org/10.1007/s12045-023-1530-7" @default.
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