Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19

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Abstract The COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. With advancements in equipment capabilities, remote communications, and on-board/distributed computing, information-based technologies are playing an increasingly critical role in identifying, detecting, and diagnosing potential COVID-19 cases. This study aims to explore factors for early diagnosis, tracking, and identification of COVID-19 spread, focusing on data collection and discussing opportunities for improvement. The study recognizes that deep learning models are well-suited for mitigating the impact of COVID-19, given the availability of a large volume of pandemic data through various technologies and collaborative efforts. While deep learning and big data approaches may not have been extensively implemented or clinically tested, they offer quick responses and valuable insights to medical staff and decision-makers. However, designing deep learning algorithms for COVID-19 presents numerous challenges. The quality and quantity of COVID-19 datasets need further improvement, demanding ongoing efforts from the research community to enhance data quality and reliability.

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