COVID-19 image classification using deep learning: Advances, challenges and opportunities - Aggarwal, Priya; Mishra, Narendra Kumar; Fatimah, Binish; Singh, Pushpendra; Gupta, Anubha; Joshi, Shiv Dutt (Computers in biology and medicine, 2022)
Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID-19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification. •This study presents a comprehensive review on COVID-19 image classification using prominent deep learning approaches.•The study summarizes the number of important contributions to the field by various researchers.•The work includes critical discussions and open challenges for an automated detection of COVID-19 using CT and X-ray images.•Finally, the study enumerates opportunities and directions for future research work.