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.
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Seema S. Bhat is currently working as an Assistant Professor in Dept. of Information Scienece and Engineering in Dayananda Sagar College of Engineering, Bengaluru. Her research interests include magnetic resonance imaging and medical image processing.
Hanumantharaju M. C is currently working as a Professor in Dept. of Electronics and Communication in BMS Institute of Technology and Management, Bengaluru. His research interests include VLSI, algorithm design, reconfigurable IC design and image processing.
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Bhat, S.S., Hanumantharaju, M.C. A Super-resolution-based Approach for the Detection of Covid—19 Infection From Chest X-ray Images. Reson 28, 127–148 (2023). https://doi.org/10.1007/s12045-023-1530-7
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DOI: https://doi.org/10.1007/s12045-023-1530-7