Retracted: A Novel Benchmarking Framework for Selecting the Best Deep Learning Model Diagnosing COVID-19 Based on New Development for Dual MCDM Methods

Authors

Mahmood M. Salih, Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit, IraqFollow
Yousif Raad Muhsen, College of Computer Science and Information Technology, Wasit University, Wasit, Iraq AND Technical Engineering College, Al-Ayen University, Thi-Qar, IraqFollow
M.A. Ahmed, Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit, IraqFollow
Reem D. Ismael, Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit, IraqFollow
Moceheb Lazam Shuwandy, Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit, IraqFollow
Z.T. Al-qaysi, Computer Science Department, College of Computer Science and Mathematics, Tikrit University (TU), Tikrit, Iraq

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 1^st phase is related to identifying a new decision matrix containing 24 evaluation criteria to evaluate the ten deep learning models. Furthermore, the 2^nd 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 2^nd and 3^rd 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.

Reason for Retraction

This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction Guidelines.

Please see the Retraction Notice available at:

https://ijcsm.researchcommons.org/ijcsm/vol7/iss1/3/

DOI: https://doi.org/10.52866/2788-7421.1348

Recommended Citation

Salih, Mahmood M.; Muhsen, Yousif Raad; Ahmed, M.A.; Ismael, Reem D.; Shuwandy, Moceheb Lazam; and Al-qaysi, Z.T. (2025) "Retracted: A Novel Benchmarking Framework for Selecting the Best Deep Learning Model Diagnosing COVID-19 Based on New Development for Dual MCDM Methods," Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 2.
DOI: https://doi.org/10.52866/2788-7421.1276
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/2