Machine Learning-Based COVID-19 Prediction from Chest X-Ray Images

The rapid spread of COVID-19 has underscored the need for efficient and accurate diagnostic methods. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) remains the gold standard for COVID-19 diagnosis, it is often time-consuming and resource-intensive. In this paper, we propose an automated approach for COVID-19 detection using chest X-ray (CXR) images, leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs). Our model is designed to classify CXR images into three categories: COVID-19, pneumonia, and normal cases. We utilized publicly available datasets and incorporated data augmentation techniques to improve model generalization and robustness. The proposed system achieves high performance across multiple evaluation metrics, including precision, recall, and F1-score, demonstrating its potential as a fast, cost-effective, and non-invasive diagnostic tool. This work highlights the promise of deep learning for COVID-19 screening, particularly in resource-limited settings where access to RT-PCR may be constrained Keywords COVID-19 detection, chest X-ray, deep learning, convolutional neural networks (CNNs), data augmentation, automated diagnosis, precision, recall, F1-score, resource-limited settings.