COV-ELM classifier: An Extreme Learning Machine based identification of COVID-19 using Chest X-Ray Images

Coronaviruses constitute a family of viruses that gives rise to respiratory diseases. COVID-19 is an infectious disease caused by a newly discovered coronavirus also termed Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As COVID-19 is highly contagious, early diagnosis of COVID-19 is crucial for an effective treatment strategy. However, the reverse transcription-polymerase chain reaction (RT-PCR) test which is considered to be a gold standard in the diagnosis of COVID-19 suffers from a high false-negative rate. Therefore, the research community is exploring alternative diagnostic mechanisms. Chest X-ray (CXR) image analysis has emerged as a feasible and effective diagnostic technique towards this objective. In this work, we propose the COVID-19 classification problem as a three-class classification problem namely COVID-19, normal, and pneumonia. We propose a three-stage framework, named COV-ELM based on extreme learning machine (ELM). Our dataset comprises CXR images in a frontal view, namely Poster anterior (PA) and Erect anteroposterior (AP). Stage one deals with preprocessing and transformation, stage 2 deals with the challenge of extracting relevant features which are passed as input to the ELM at the third stage, resulting in the identification of COVID-19. The choice of ELM in this work has been motivated by its significantly shorter training time as compared to conventional gradient-based learning algorithms. As bigger and diverse datasets become available, it can be quickly retrained as compared to its gradient-based competitor models. We use 10-fold cross-validation to evaluate the results of applying COV-ELM. The COV-ELM achieved a macro average F1-score of 0.95 and the overall sensitivity of ${0.94 \pm 0.02}$ at a 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform its competitors.