Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

Abstract

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

Fig 1. Illustrative block diagram of the proposed COVID-19 detection system.

Fig 2. PCA steps.

Fig 3. Feature extraction steps.

Fig 4. Diagram of the arithmetic crossover and uniform mutation operations example.

Fig 5. Pseudocode of the OGA-ELM [28].

Fig 6. OGA-ELM’s flowchart [28].

Fig 7. Description of the dataset.

Fig 8. Accuracy results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 9. Precision results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 10. Recall results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 11. F-measure results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 12. G-mean results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 13. True positive results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 14. True negative results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 15. False positive results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 16. False negative results of the OGA–ELM model using random, K-tournament, and roulette wheel.

Fig 17. ROC of the OGA–ELM for the highest result.

Fig 18. ROC of the NN for the highest result.

Fig 19. ROC of the ELM for the highest result.

Fig 20. ROC of the FLN for the highest result.

Fig 21. ROC of the SVM for the highest result.

Fig 22. ROC of the CNN for the highest result.

Fig 23. The highest achieved accuracy for all methods.