•  
  •  
 

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.

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This article has been retracted at the request of the Editorial Office, following an internal investigation conducted in accordance with the Committee on Publication Ethics (COPE) Retraction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

The investigation identified serious concerns affecting the integrity and reliability of the published work. Specifically, one or more of the following issues were confirmed:

  1. Undisclosed use of computer-generated text and/or data, in which substantial portions of the content were produced using algorithmic or artificial intelligence–based tools without transparent disclosure, contrary to the journal's authorship and transparency policies.

  2. Compromised peer-review process, indicating irregularities that undermine the validity, independence or authenticity of the review procedure.

  3. Inappropriate or misleading citations, including references that are irrelevant, improperly used, or appear to artificially inflate citation metrics, thereby distorting the scholarly record.

  4. Authorship-related concerns, including the addition of new author(s) at a later stage of the publication process without adequate justification, documentation, or transparent disclosure, raising unresolved questions regarding author contributions, responsibility, and compliance with the journal's authorship criteria.

The Editorial Office determined that these issues significantly compromise the scientific integrity of the article, and that correction alone would be insufficient to address the concerns. Retraction was therefore deemed necessary to maintain the accuracy and trustworthiness of the scholarly record.

The authors were informed of the findings and the retraction decision. While the authors do not respond to this retraction, the journal has proceeded with the retraction in line with COPE guidance, which permits retraction without author consent when editorial integrity is at risk.

This retraction is issued to alert readers that the findings and conclusions of the article should not be relied upon. The original article will remain accessible for the sake of the scholarly record, but it will be clearly marked as retracted.

Apologies are offered to readers of the journal that this was not detected during the submission process.

Please see the Retraction Notice available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/2.

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

Download

Share

COinS