Limitations and challenges on the diagnosis of COVID-19 using radiology images and deep learning

The world is facing a great threat nowadays. The COVID-19 virus outbreak that occurred in Wuhan in China in December 2019 continues to increase in the middle of 2020. Within the scope of this epidemic, different contents of data are published and products for improving the treatment process. One of the major symptoms of COVID-19 epidemic disease, which was revealed by the World Health Organization, is intense cough and breathing difficulties. Chest X-ray (CXR) and computing tomography (CT) images of patients infected with COVID-19 are also a type of data that allows data scientists to work with healthcare professionals during this struggle. Fast evaluation of these images by experts is important in the days when the epidemic has suffered. This chapter focuses on artificial intelligence (AI) for a successful and rapid diagnostic recommendation as part of these deadly epidemic prevention efforts that have emerged. As a study case, a dataset of 373 CXR images, 139 of which were COVID-19 infected, collected from open sources, was used for diagnosis with deep learning approaches of COVID-19. The use of EfficientNet, an up-to-date and robust deep learning model for education, offers the possibility to become infected with an accuracy of 94.7%. Nevertheless, some limitations must be considered when producing AI solutions by making use of medical data. Using these results, a perspective is provided on the limitations of deep learning models in the diagnosis of COVID-19 from radiology images for data quality, amount of data, data privacy, explainability, and robust solutions.