Detection of COVID-19 using chest X-rays enhanced by histogram equalization and convolutional neural networks

Abstract

The persistent global health crisis initiated by the COVID-19 pandemic continues to demand robust and high-throughput diagnostic solutions. While gold-standard methods, such as polymerase chain reaction (PCR) testing, are accurate, their scalability and turnaround time remain limitations in high volume settings. This paper introduces a novel deep learning framework designed for rapid and accurate detection of COVID-19 from chest X-ray (CXR) imagery. Our methodology leverages a convolutional neural network (CNN) architecture, augmented by a crucial pre-processing stage: histogram equalization. This step is vital for enhancing the subtle contrast features inherent in CXR scans, there by significantly improving the quality of the input data and facilitating superior feature extraction by the CNN. The model was trained and rigorously validated on a dedicated dataset. Performance was systematically quantified using a comprehensive confusion matrix, yielding key metrics such as precision and specificity, alongside the receiver operating characteristic (ROC) curve. The achieved results are highly encouraging, demonstrating a classification accuracy of 98.45%. This innovative approach offers a substantial acceleration of the diagnostic process, providing a non-invasive and highly effective complementary tool for clinicians. Ultimately, this advancement has the potential to streamline patient management protocols and alleviate diagnostic pressures on global healthcare infrastructures.