Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19.

Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model.

Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%.

Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe.

目的：在当前的大流行期间，全球的科学家、研究人员和卫生专业人员都在寻找应对 COVID-19 的新技术方法。机器学习和深度学习方法在之前的流行病中报告的出色表现鼓励研究人员使用这些方法开发系统来诊断 COVID-19。

方法：在本文中，提出了一种基于集成的多级投票模型来从胸部 X 光诊断 COVID-19。本文提出的多级投票模型是使用四种机器学习算法构建的，即随机森林 (RF)、逻辑回归 (LR)、具有线性核的支持向量机 (SVM) 和 K 最近邻 (KNN)。这些算法使用 ResNet50 深度学习模型提取的特征进行训练，然后将它们合并形成投票模型。在这项工作中，投票在两个级别进行，在级别 1，这四种算法分为 2 组，每组包含两种算法（第 1 组——具有线性核和 LR 的 SVM，第 2 组——RF 和 KNN）和组内硬投票被执行。在第 2 级，这两个集合使用硬投票合并以形成建议的模型。

结果：所提出的多级投票模型优于所有机器学习算法、预训练模型和其他提出的工作，准确率为 100%，特异性为 100%。

结论：所提出的模型有助于在全球范围内更快地诊断 COVID-19。