<![CDATA[This study presents an automated deep learning approach for the rapid detection of COVID-19 using chest X-ray images. Given the urgent need for efficient disease diagnostics, we utilize convolutional neural networks (CNNs) to develop a ResNet-based classification model. Our dataset includes ten images, five depicting COVID-19 cases and five standard X-ray images, chosen for their rapidity and low radiation dose. The model demonstrates high accuracy, successfully classifying all images. Transfer learning techniques further enhance performance, indicating the potential for broader application and improved diagnostic capabilities. This research addresses the current knowledge gap in automated COVID-19 diagnostics, offering a reliable method for swift and accurate detection, with implications for enhancing disease management strategies. Highlights: Rapid COVID-19 detection using deep learning. High accuracy with convolutional neural networks. Quick results with low radiation in chest X-rays. Keywords: COVID-19, deep learning, chest X-ray, automated diagnosis, convolutional neural networks]]>
Copyrights © 2024
