COV-ELM classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images

Abstract

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.

A survey of machine learning techniques for detecting and diagnosing COVID‐19 from imaging

“…Most previous methods divided the dataset into various ratios of training, testing, and validation, while other methods used cross‐validation. While considering cross‐validation, the preceding studies analyzed 10‐fold [44,49,67,70], 5‐fold [31,32,50,77,79], and 4‐fold [45] cross‐validation. Compared to 4‐fold cross‐validation, 10‐fold and 5‐fold cross‐validation provide better results.…”

“…One of the previous studies [88] used Inception, and its AUC reached 100%. The sensitivity of feature extraction using NASNet‐Large [38] is 100%, while the F1‐score using the COV‐ELM [70] method is 95%. In addition, in terms of accuracy, NASNetMobile [26] scored the highest, reaching 100%.…”

“…Also, in [111], the authors incorporated CNN with k‐nearest neighbors (k‐NN) and a support estimator network, but it requires a lot of data to train. In the study [70], the authors applied the COV‐ELM classifier, which applies an extreme learning machine (ELM) to classify COVID‐19 from chest X‐rays and adjust the network with minimum interference, thereby reducing training time. An end‐to‐end web‐based detection system with a bagging trees classifier was promoted to simulate the digital clinical pipeline and facilitate the screening of suspicious cases in [49].…”

“…[4,[15][16][17]19,20,22,[24][25][26][27][28]34,35,[37][38][39]41,43,[44][45][46]48,49,52,55,58,61,63,69,74,78,80,81,83,85,86,89,90,92,93,95,[99][100][101]105,107,111,[113][114][115] Multi-class[14,18,21,…”

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

“…They show that the implementation of parallel versions of the algorithm in the C language with the OpenBLAS, Intel MKL, and MAGMA libraries is more advantageous compared to the reference version of MATLAB. Afterward, Rajpal et al [106] addressed the problem of ELM-based COVID-19 classification (COV-ELM) into three classes: (1) COVID-19, (2) normal, and (3) pneumonia. The results showed that COV-ELM outperforms new-generation machine learning algorithms.…”