Phumkuea Thanakorn, Wongsirichot Thakerng, Damkliang Kasikrit, Navasakulpong Asma
College of Digital Science, Prince of Songkla University, Songkhla, Thailand.
Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand.
JMIR Form Res. 2023 Feb 28;7:e42324. doi: 10.2196/42324.
The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention.
We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and healthy patients from CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models.
A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients.
The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis system, which was made publicly available.
The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.
新型冠状病毒肺炎（COVID-19）大流行引发了全球关注，中重度病例表现出肺部炎症和呼吸衰竭。胸部X线（CXR）成像对诊断至关重要，通常由经验丰富的医学专家进行解读。机器学习已被应用且准确率尚可，但计算效率较少受到关注。
我们引入了一种新型混合机器学习模型，以从CXR图像中准确分类COVID-19患者、非COVID-19患者和健康个体，同时减少计算时间并取得了有前景的结果。我们提出的模型经过了全面评估，并与现有模型进行了比较。
进行了一项回顾性研究，使用包括决策树、支持向量机和神经网络在内的机器学习技术分析了5个包含4200张CXR图像的公共数据集。对图像进行预处理，包括图像分割、增强和特征提取。选择性能最佳的机器学习技术并将其组合成一个用于COVID-19的多层混合分类模型（MLHC-COVID-19）。该模型由两层组成。第一层旨在区分健康个体和感染患者，而第二层旨在对COVID-19患者和非COVID-19患者进行分类。
MLHC-COVID-19模型在未见过的COVID-19 CXR图像上进行了训练和评估，分别实现了相当高的准确率和F值，分别为0.962和0.962。这些结果表明MLHC-COVID-19在分类COVID-19 CXR图像方面的有效性，提高了准确率并减少了解读时间。该模型还被嵌入到一个基于网络的MLHC-COVID-19计算机辅助诊断系统中，并已公开提供。
该研究发现MLHC-COVID-19模型有效地将COVID-19患者的CXR图像与健康个体和非COVID-19个体的图像区分开来。它优于其他先进的深度学习技术，并显示出有前景的结果。这些结果表明，MLHC-COVID-19模型可能有助于COVID-19患者的早期检测和诊断，从而在控制和管理大流行中发挥重要作用。尽管大流行已有所缓解，但该模型可进行调整并用于未来类似情况。该模型还被集成到一个可公开访问的基于网络的计算机辅助诊断系统中。
