CAIT, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia.
Department of Communication Engineering, School of Electrical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia.
PLoS One. 2020 Dec 15;15(12):e0242899. doi: 10.1371/journal.pone.0242899. eCollection 2020.
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.
冠状病毒病（COVID-19）是一种由严重急性呼吸系统综合征引起的持续全球大流行疾病。胸部计算机断层扫描（CT）是一种有效的检测肺部疾病的方法，包括 COVID-19。然而，CT 扫描昂贵且耗时。因此，这项工作专注于使用胸部 X 射线图像检测 COVID-19，因为它广泛可用，比 CT 扫描更快且更便宜。许多机器学习方法，如深度学习、神经网络和支持向量机；已经使用 X 射线来检测 COVID-19。尽管这些方法在准确性方面的性能可以接受，但是它们需要高计算时间和更多的内存空间。因此，这项工作采用了三种选择标准（即随机、K-锦标赛和轮盘赌）的优化遗传算法-极限学习机（OGA-ELM）来使用 X 射线图像检测 COVID-19。极限学习机（ELM）的最重要的关键优势是：（i）ELM 在避免过拟合方面的能力很高；（ii）它在二进制和多类分类器中的可用性；以及（iii）ELM 可以作为具有神经网络结构的核支持向量机工作。这些优势使 ELM 在实现出色的学习性能方面非常高效。ELM 已经成功应用于许多领域，包括乳腺癌检测、病理性脑检测和原位导管癌检测等医学领域，但尚未在 COVID-19 检测中进行测试。因此，这项工作旨在确定使用胸部 X 射线图像使用 OGA-ELM 检测 COVID-19 的有效性。为了降低直方图定向梯度特征的维数，我们使用主成分分析。OGA-ELM 的性能在包含 188 张胸部 X 射线图像的基准数据集上进行评估，这些图像分为两类：健康和 COVID-19 感染。实验结果表明，OGA-ELM 以快速的计算时间实现了 100.00%的准确率。这表明 OGA-ELM 是一种使用胸部 X 射线图像检测 COVID-19 的有效方法。
