Intelligent Decision-Making Framework for Evaluating and Benchmarking Hybridized Multi-Deep Transfer Learning Models: Managing COVID-19 and Beyond

Abstract

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 [Formula: see text] was set to 1 and the gamma parameter [Formula: see text] 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[Formula: see text]GHz, 8[Formula: see text]GB of RAM, and a graphical processing unit of 2[Formula: see text]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.