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 to distinguish between COVID-19, normal, and pneumonia classes. 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 Posteroanterior (PA) and Erect anteroposterior (AP). Stage one deals with preprocessing and transformation while stage two deals with feature extraction. These extracted features are passed as an 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 faster convergence, better generalization capability, and shorter training time in comparison to the conventional gradient-based learning algorithms. As bigger and diverse datasets become available, ELM can be quickly retrained as compared to its gradient-based competitor models. We use 10-fold cross-validation to evaluate the results of COV-ELM. The proposed model achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 ± 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 in this three-class classification scenario. Further, LIME has been integrated with the proposed COV-ELM model to generate annotated CXR images. The annotations are based on the superpixels that have contributed to distinguish between the different classes. It was observed that the superpixels correspond to the regions of the human lungs that are clinically observed in COVID-19 and Pneumonia cases.

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.

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