## 5 Conclusions and Future Work  
With the growing threat of new coronavirus strains throughout the world, early diagnosis of COVID-19 is critical especially due to the global scarcity of healthcare facilities. In this work, we propose a hybrid fuzzy ensemble-based method for detecting COVID-19-positive patients using CXR images. Ensemble learning can improve the overall performance by combining the prominent properties of its constituent models, resulting in better predictions than the individual contributing models. Moreover, ensemble models can enhance performance by lowering the variance of prediction errors by adding some bias to compete with base learners. In the proposed work, we used various transfer learning models, which were separately trained to make independent predictions. The models were then integrated to forecast a class value, utilizing a novel approach combining Majority Voting, Sugeno Integral, and Choquet Fuzzy. A publicly accessible database containing 6939 CXR pictures was employed to assess the effectiveness of the proposed method. The data were divided into training and test sets in proportions of 80% and 20%, respectively. The proposed method, Xception + Choquet Fuzzy ensemble methods, obtained a better accuracy of 99.57% compared to other transfer learning models. The suggested methodology failed to categorize 0.43% of test data, which may be due to the relatively poor performance of the individual transfer learning models. As a result, creating or selecting better individual transfer learning models may help to amend this issue. In addition, only three basic transfer learning models were applied in the aforementioned ensemble method. Therefore, adding more classifier techniques might be an additional option to improve the current method. To demonstrate its robustness across various domains, the proposed model might be applied to various classification-based image analyses. Such advancements will aid radiologists to develop a graphical user interface (GUI) for recognizing COVID-19 to keep up with the disease's various stages of progression and imagistic patterns.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Authors** & **Best Methodology** & **Accuracy (\%)** \\ \hline [PERSON] et al. [43] & ResNet50+ SVM & 95.38\% \\ \hline [PERSON] et al. [44] & Deep CNN; ResNet-50 & 98\% \\ \hline [PERSON] et al. [45] & K-Nearest Neighbour & 98.09\% \\ \hline [PERSON] et al. [46] & Convolutional Neural Network & 93.2\% \\ \hline [PERSON] et al. [30] & Neural Search Architecture & 97\% \\  & Network-Type CNN & 96.3\% \\ \hline [PERSON] et al. [47] & VGG19 & 96.3\% \\ \hline [PERSON] et al. [48] & SqueezeNet & 97\% \\ \hline [PERSON] et al. [49] & VGG16 & 95\% \\ \hline [PERSON] et al. [21] & Deep Boosted Hybrid Learning & 98.53\% \\ \hline [PERSON] et al. [50] & Binary-Classification with CGANs & 98.7\% \\ \hline [PERSON] et al. [51] & CNN with pre\(-\)trained ResNet50 & 97.1\% \\ \hline [PERSON] et al. [24] & VGG19 and XGBoost + PSO & 98.71\% \\ \hline [PERSON] et al. [34] & 15 distinct deep CNN models & 89.3\% \\ \hline [PERSON] et al. [35] & DenseNet201 model & 94\% \\ \hline [PERSON] et al. [52] & Binary differential-CNN & 99.43\% \\ \hline [PERSON] et al. [53] & MobileNetV2 & 94.72\% \\ \hline Proposed Model & Xception + Choquet Fuzzy & 99.57\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparisons of various transfer learning models with the suggested model using CXR images.  
**Author Contributions:** Conceptualization, R.K. and C.M.; methodology, P.G.A. and A.H.G. software, R.K.; validation, C.M. and A.H.G.; formal analysis, P.G.A.; investigation, A.H.G.; resources, C.M.; data curation, R.K.; writing--original draft preparation, A.H.G.; writing--review and editing, A.H.G.; visualization, P.G.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.