In this study, we developed a novel multi-criteria decision-making (MCDM) framework for evaluating and benchmarking hybrid multi-deep transfer learning models using radiography X-ray coronavirus disease (COVID-19) images. First, we collected and pre-processed eight public databases related to the targeted datasets. Second, convolutional neural network (CNN) models extracted features from 1,338 chest X-ray (CXR) frontal view image data using six pre-trained models: VGG16, VGG19, painters, SqueezeNet, DeepLoc, and Inception v3. Then, we used the intersection between the six CNN models and eight classical machine learning (ML) methods, including AdaBoost, Decision Tree, logistic regression, random forest, kNN, neural network, and Naive Bayes, to introduce 48 hybrid classification models. In this study, eight supervised ML methods were used to classify COVID-19 CXR images. The classifiers were implemented using the TensorFlow2 and Keras libraries in Python. A feature vector was extracted from each image, and a five-fold cross-validation technique was used to evaluate the performance. The cost parameter $c$ was set to 1 and the gamma parameter $\gamma$ was set to 0.1 for all classifiers. The experiments were run on a Windows-based computer with dual Intel I CoITM i7 processors at 2.50$$GHz, 8$$GB of RAM, and a graphical processing unit of 2$$GB. The performance metrics of the 48 hybrid models, including the classification accuracy (CA), specificity, area under the curve (AUC), F1 score, precision, recall, and log loss, were used as efficient evaluation criteria. Third, the MCDM approach was used, which included (i) developing a dynamic decision matrix based on seven evaluation metrics and the developed hybrid models, (ii) developing the fuzzy-weighted zero-inconsistency method for determining the weight coefficients for the seven-evaluation metrics with zero inconsistency, and (iii) developing the Višekriterijumsko Kompromisno Rangiranje method for benchmarking the 48 hybrid models. Our experimental results reveal that (i) CA and AUC obtained the highest importance weights of 0.164 and 0.147, respectively, whereas F1 and specificity obtained the lowest weights of 0.134 and 0.134, respectively, and (ii) the highest three hybrid model scores were painters neural network, painters logistic regression, and VGG16-logistic regression, making them the highest ranking scores. Finally, the developed framework was validated using sensitivity analysis and comparison analysis.

在这项研究中，我们开发了一种新颖的多标准决策 (MCDM) 框架，用于使用 X 射线冠状病毒疾病 (COVID-19) 图像评估和基准化混合多重深度迁移学习模型。首先，我们收集并预处理了与目标数据集相关的八个公共数据库。其次，卷积神经网络 (CNN) 模型使用 6 个预训练模型：VGG16、VGG19、painters、SqueezeNet、DeepLo​​c 和 Inception v3 从 1,338 个胸部 X 光 (CXR) 正面视图图像数据中提取特征。然后，我们利用六种CNN模型和八种经典机器学习（ML）方法（包括AdaBoost、决策树、逻辑回归、随机森林、kNN、神经网络和朴素贝叶斯）的交集，引入了48种混合分类模型。在这项研究中，使用八种监督 ML 方法对 COVID-19 CXR 图像进行分类。分类器是使用 Python 中的 TensorFlow2 和 Keras 库实现的。从每个图像中提取特征向量，并使用五折交叉验证技术来评估性能。成本参数$C$设置为 1 并且 gamma 参数$\gamma$所有分类器都设置为 0.1。实验在一台基于 Windows 的计算机上运行，​​该计算机配备双 Intel I CoITM i7 处理器，频率为 2.50$$千兆赫，8$$GB 的 RAM，以及 2 个图形处理单元$$GB。48个混合模型的性能指标，包括分类精度（CA）、特异性、曲线下面积（AUC）、F1分数、精度、召回率和对数损失，被用作有效的评估标准。第三，使用MCDM方法，其中包括（i）开发基于七个评估指标和开发的混合模型的动态决策矩阵，（ii）开发模糊加权零不一致方法来确定七个评估指标的权重系数零不一致的评估指标，以及 (iii) 开发Višekriterijumsko Kompromisno Rangiranje对 48 种混合动力车型进行基准测试的方法。我们的实验结果表明，(i) CA 和 AUC 分别获得最高重要性权重 0.164 和 0.147，而 F1 和特异性分别获得最低权重 0.134 和 0.134，(ii) 得分最高的三个混合模型是画家神经网络、画家逻辑回归和VGG16逻辑回归，使它们成为排名最高的得分。最后，使用敏感性分析和比较分析对所开发的框架进行了验证。