Published Paper Details:

The rapid spread of COVID-19 has prompted urgent research efforts to develop effective diagnostic tools for timely identification and management of the disease. Chest X-ray imaging has emerged as a valuable modality for detecting COVID-19 pneumonia, offering a non-invasive and widely available means of screening and diagnosis. Deep learning models, particularly convolutional neural networks (CNNs), have shown promising results in automated detection of COVID-19 from chest X-ray images. This review paper provides a comprehensive overview of the current trends and future perspectives in the application of deep learning models for COVID-19 detection from chest X-ray images. We systematically analyze the literature to highlight the evolution of deep learning techniques, the performance of different CNN architectures, and the challenges and limitations encountered in COVID-19 diagnosis. Additionally, we discuss emerging trends, such as transfer learning, ensemble methods, and multimodal approaches, and their potential impact on improving the accuracy and reliability of COVID-19 detection. By synthesizing existing research findings and identifying areas for future exploration, this review aims to provide valuable insights for researchers, clinicians, and policymakers involved in combating the COVID-19 pandemic through advanced diagnostic strategies.