Improving COVID-19 Detection: Leveraging Convolutional Neural Networks in Chest X-Ray Imaging

The global impact of the COVID-19 pandemic has significantly disrupted healthcare systems worldwide. Amidst challenges, there is a crucial demand for efficient methodologies to expedite disease detection. This research underscores the potential of Deep Neural Networks in enhancing pandemic management over the past five years. Focusing on Artificial Intelligence (AI) application in COVID-19 detection through X-ray imaging, this research advocates using Visual Geometry Group (VGG'16), a Convolutional Neural Network (CNN) used for image classification with multiple layers. These CNNs act as classifier-based systems, treating images as structured data arrays to identify and learn patterns. Quantifying the model's effectiveness through the accuracy score, this research reveals a 0.90% accuracy, indicating the model's accurate detection of COVID-19 cases in X-ray images. Additionally, the study highlights a significant achievement with a less than 10% false positive rate, crucial for reliable and prompt COVID-19 diagnoses in the healthcare industry. In conclusion, this research presents an AI-driven approach, utilizing VGG'16 and convolutional neural networks to enhance the efficiency and accuracy of COVID-19 detection in X-ray imaging. The high accuracy score and low false positive rate positions this methodology as a valuable contribution, offering robust pandemic management and healthcare decision-making.