  The COVID19 pandemic globally and significantly has affected the life and
health of many communities. The early detection of infected patients is
effective in fighting COVID19. Using radiology (X-Ray) images is perhaps the
fastest way to diagnose the patients. Thereby, deep Convolutional Neural
Networks (CNNs) can be considered as applicable tools to diagnose COVID19
positive cases. Due to the complicated architecture of a deep CNN, its
real-time training and testing become a challenging problem. This paper
proposes using the Extreme Learning Machine (ELM) instead of the last fully
connected layer to address this deficiency. However, the parameters' stochastic
tuning of ELM's supervised section causes the final model unreliability.
Therefore, to cope with this problem and maintain network reliability, the
sine-cosine algorithm was utilized to tune the ELM's parameters. The designed
network is then benchmarked on the COVID-Xray-5k dataset, and the results are
verified by a comparative study with canonical deep CNN, ELM optimized by
cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale
optimization algorithm. The proposed approach outperforms comparative
benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset,
leading to a relative error reduction of 2.33% compared to a canonical deep
CNN. Even more critical, the designed network's training time is only 0.9421
milliseconds and the overall detection test time for 3100 images is 2.721
seconds.
