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

Abstract

Coronaviruses constitute a family of virus that gives rise to respiratory diseases. Coronavirus disease 2019 is an infectious disease caused by a newly discovered coronavirus also termed as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its rapid spread, WHO has declared COVID-19 outbreak a pandemic on 11th March 2020. Reverse transcription-polymerase chain reaction (RT-PCR) test is popularly used worldwide for the detection of COVID-19. However, due to the high false-negative rate of RT-PCR test, chest X-ray (CXR) imaging is emerging as a feasible alternative for the detection of COVID-19. In this work, we propose a multiclass classification model COV-ELM, based on the extreme learning machine which classifies the CXR images into one of the three classes, namely COVID-19, normal, and pneumonia. The choice of ELM in this work has been motivated by its significantly short training time as compared to conventional gradient-based learning algorithms. After some preprocessing, we extract a pool of features based on texture and frequency. This pool of features serves as an input to the ELM and a 10-fold cross-validation method is employed to evaluate the proposed model. For experimentation, we use chest X-ray (CXR) images from three publicly available sources. The results of applying COV-ELM on test data are quite promising. The COV-ELM achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 ± 0.02 at 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform in a three-class classification scenario. The main advantage of COV-ELM is that its training time being quite low, as bigger and diverse datasets become available, it can be quickly retrained as compared to its gradient-based competitor models.

Extreme learning machine for the characterization of anomalous diffusion from single trajectories (AnDi-ELM)

“…Therefore, ELM offers the advantage of low computational cost as compared to other ML approaches. Although its shallow architecture affects its performance for complex tasks, ELM has been applied to several real-world problems, including the identification of COVID-19 using features extracted from chest x-ray images [22], showing a good generalization capability [21].…”

Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories

“…Therefore, ELM offers the advantage of low computational cost as compared to other ML approaches. Although its shallow architecture affects its performance for complex tasks, ELM has been applied to several real-world problems, including the identification of COVID-19 [20], showing a good generalization capability [19].…”