The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for further analysis.
翻译:COVID-19 疾病在中国武汉首次发现,并迅速蔓延到全世界。在COVID-19 流行病后,许多研究人员开始寻找一种方法,利用胸前X光图像诊断COVID-19 。早期诊断该疾病可以大大影响治疗过程。在本篇文章中,我们提出了一种比文献中报告的其他方法更快和更准确的新方法。拟议方法使用DenseNet169 和移动网络深神经网络的组合来提取病人X光图像的特征。在使用单向特征选择算法后,我们改进了最重要的特征。然后,我们将选定特征用作对光GBM(轻度推动机)算法的投入。为评估拟议方法的有效性,使用了ChestX-光8 数据集,其中包括病人胸部的1125 X光图像。拟议方法在两类(COVID-19,健康价值)和多级(COVID-19)中达到了98.54%和91.11%的缩略图。我们分别使用了“高度”的GRAMA-GRA级(C) 分别用于健康等级分析。