Beyond the Human Eye: Using CNNs to Decipher COVID-19 in Chest X-rays

Deep Learning

The COVID-19 pandemic has posed challenges, for healthcare systems worldwide requiring effective diagnostic methods and patient management strategies. As a result, the analysis of large-scale data statistics and the precise identification of COVID-19 patients using chest X-rays have become crucial in the fight against the virus. Radiologists, who are at the forefront of this battle face the task of sifting through several X-ray images to accurately identify cases of COVID-19. While traditional diagnostic procedures like Polymerase Chain Reaction (PCR) tests are accurate they suffer from time delays and resource limitations. Chest X-rays offer a cost-effective alternative; however, radiologists may encounter difficulties in interpreting them due to the features that indicate COVID-19-related pneumonia. To address these challenges, we propose a Conventional Convolutional Neural Networks (CNNs) model in deep learning using Adam optimizer. We aim to improve management efficiency enable early detection and ultimately enhance patient outcomes in detecting the disease.

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