Diagnosing Coronavirus (COVID-19) Using Various Deep Learning Models: A Comparative Study

Abstract

The past decade has shown considerable growth in the field of deep learning techniques, changing the context of several areas of research. In medicine, deep learning techniques have achieved encouraging results with additional precision in the processing of various image datasets, such as brain MRI, chest X-ray, and retinal imaging. For example, many human organs can be scanned quickly and at a lower cost using X-ray machines, which are widely available in hospitals and clinics. It is common practice for expert radiologists to interpret various radiographic images manually. Training a deep learning network with these images provides medical staff with valuable help in diagnosing COVID-19 patients. Such a scenario helps the developing countries, due to the availability of X-ray machines, but there is a large shortage in the availability of the experts. This study aims to compare the effectiveness of four widely used deep learning models in terms of accuracy and processing time to determine the best. These models include ResNet-50, AlexNet, GoogLeNet, and VGG16. The findings indicate that the processing time is proportional to the accuracy. ResNet-50 had the highest diagnostic accuracy but was the slowest in processing time. Both CPU (serial) and GPU (parallel) platforms are used in this comparative study. When the models used a CPU platform, the processing time was in the range of 1–1.5 s, but it dramatically decreased to the range of 7.1–20.7 ms when running on a parallel platform (GPU). Hence, the GPU platform is very successful and was deemed best suited for real-time diagnostic applications.