Coronaviruses constitute a family of virus that particularly gives rise to respiratory diseases. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus also termed as Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid spread, COVID-19 outbreak has been declared a pandemic on 11th March 2020. The reverse transcription-polymerase chain reaction (RT-PCR) test is most commonly used for the qualitative assessment of the presence of SARS-CoV-2. Due to the high false-negative rate of RT-PCR test, chest X-ray (CXR) imaging has emerged to be a feasible alternative for the detection of COVID-19. In this work, we propose a multi-classification model based on extreme learning machine, COV-ELM, that aims to classify the CXR images belonging to three classes, namely COVID-19, normal, and pneumonia. The choice of ELM in this work is based on the fact that ELM significantly shortens the training time with the least interventions required to tune the networks as compared to conventional gradient-based learning algorithms. The proposed work is experimented on the COVID-19 chest X-ray (CXR) image data collected from three publicly available sources. The image data is preprocessed and local features are extracted by exploiting the frequency and texture regions to generate a feature pool. This pool of features is provided as an input to the ELM and a 10-fold cross-validation method is employed to evaluate the proposed model. The COV-ELM achieved a macro average of f1-score is 0.95 and the overall sensitivity of the COV-ELM is at 95% confidence interval. The COV-ELM outperforms other competitive machine learning algorithms in a three-class classification scenario. The results of COV-ELM are quite promising which increases its suitability to be applied to bigger and more diverse datasets.
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