A review on The Role of Deep Learning a CNN Algorithm in Diagnosing COVID-19

Abstract

Abstract

The COVID-19 pandemic has led to massive global loss of life and strained public health systems. Rapid detection and diagnosis are essential to protect both patients and healthcare providers. While COVID-19 testing is more available, limitations in laboratory kit supplies and the high cost and time of RT-PCR testing remain significant. As an alternative, chest X-ray imaging is commonly used, yet manually analyzing these images is difficult. Therefore, Computer-Aided Diagnosis (CAD) systems are vital for automated detection,This paper reviews the application of deep learning, especially convolutional neural networks (CNNs), in identifying COVID-19 from chest X-ray images. CNNs have proven effective in extracting accurate image features, enhancing diagnostic precision, enabling early detection, minimizing human errors, and recognizing disease-specific patterns. These technologies support faster diagnosis, particularly in low-resource environments. The study also explores current challenges such as the need for varied and comprehensive datasets for model training. Finally, it highlights the potential of deep learning tools in strengthening healthcare responses during pandemics.