From Data to Diagnosis: A Deep Dive into Deep Learning for COVID-19 Detection

The COVID-19 pandemic has highlighted the need for rapid and accurate diagnostic tools to manage and contain infectious diseases effectively. Deep learning (DL) has emerged as a promising solution for COVID-19 detection, particularly through the analysis of medical imaging modalities such as chest X-rays (CXR) and computed tomography (CT) scans. This paper provides a comprehensive review of the state-of-the-art in deep learning for COVID-19 detection, covering key datasets, methodologies, challenges, and future directions.The review discusses widely used datasets, such as the COVID-19 Image Data Collection, BIMCV-COVID19+, and MosMedData, highlighting their strengths and limitations. Common challenges include the scarcity of annotated data, class imbalance, and variations in imaging protocols, all of which hinder model robustness and generalizability. Additionally, issues such as model interpretability, clinical validation, and regulatory approval are examined, emphasizing the importance of explainability and real-world applicability.Promising advancements in synthetic data generation, data augmentation, explainable AI, and federated learning are explored as potential solutions to these challenges. Furthermore, multi-modal and multi-task learning approaches are discussed as avenues for creating comprehensive diagnostic systems that integrate imaging data with clinical and demographic information. The paper also underscores the importance of seamless integration of AI tools into clinical workflows to ensure their practical use in healthcare settings.This review concludes by discussing the broader implications of these technologies beyond COVID-19, emphasizing their potential to revolutionize medical diagnostics and prepare for future public health challenges. By addressing current limitations and leveraging collaborative efforts, deep learning can play a pivotal role in enhancing global healthcare systems, ensuring better outcomes for patients and clinicians alike.