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Abstract

COVID-19 was diagnosed using deep learning models by a group of studies. Evaluating and benchmarking these models are essential to achieving the most suitable model for diagnosing coronavirus. Objective: In this investigation, we offer an inclusive valuation of several deep learning models to detect the maximum appropriate and active model which gratifies doctors’ requirements and assessment criteria. Method: This study combines Fuzzy decision by the opinion score method (FDOSM) and Fuzzy-Weighted Zero-Inconsistency (FWZIC). According to the advantage of Trapezoidal Intuitionistic fuzzy, we developed FWZIC into Trapezoidal Intuitionistic fuzzy named (TrIF-FWZIC) for weighting criteria and FDOSM into Trapezoidal Intuitionistic fuzzy FDOSM (TrIF-FDOSM) to evaluate and benchmark the effectively deep learning models and tackle the issue of uncertainty. Fundamentally, the methodology of this study is presented in 2 phases; the 1st phase is related to identifying a new decision matrix containing 24 evaluation criteria to evaluate the ten deep learning models. Furthermore, the 2nd phase is related to the development of TrIF-FWZIC and TrIF-FDOSM in two main stages. Result: The findings of this study were: (1) For the individual decision-maker, the best one was Xception for the first decision-maker with a score (i.e., 0.267510407). The optimal algorithm for the 2nd and 3rd decision-makers was ResNet-101 with scores (i.e., 0.316710828, 0.457770263), respectively. (2) The best deep learning model, depending on the group decision-making, was ResNet-101 with a score (i.e., 0.32574743). Conclusion: The proposed methodology undergoes validation, sensitivity analysis, and comparative evaluation. This research enhances the selection of effective models for COVID-19 diagnosis, catering to individual and collective decision-making scenarios.

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