## 1 Introduction
[16], an SVM-based hybrid differential evolution PSO model by [PERSON] et al. [17], and a novel MAnet based two-stage classification technique for coronavirus detection utilizing CXR images by [PERSON] et al. [18]. Moreover, a computer-aided diagnostic combined approach based on graph CNN and pre-trained CNN model ([PERSON] et al. [19]), a deep Covix-net model ([PERSON] et al. [20]), new deep hybrid and deep boosted hybrid learning models ([PERSON] et al. [21]), and a gradient weighted class activation mapping technique ([PERSON] et al. [22]) for coronavirus detection exhibited accuracies of 97.60%, 97%, 98.53%, and 95%, respectively. Likewise, a DenseNet-201 architecture reported by [PERSON] et al. [23], a PSO-based eXtreme Gradient Boosting model recommended by [PERSON] et al. [24], an automatic AI-based system using majority voting ensemble techniques suggested by [PERSON] et al. [25], and a bi-level prediction model by [PERSON] et al. [26] exhibited respective accuracies of 94.96%,98.71%, 91.329%, and96.74%to diagnose COVID-19. Other novel techniques to detect coronavirus include a deep LSTM model ([PERSON] et al. [27]), Inception-v3 model based on deep CNN associated with Multi-Layered Perceptron model called CovScanNet ([PERSON] et al. [28]), and a hybrid deep CNN technique with discrete wavelet transform features ([PERSON] et al. [29]. For COVID-19 detection in patients, [PERSON] et al. [30] recommended a CovXNet model, and [PERSON] and [PERSON] et al. [31] suggested a MobileNetV2 deep CNN model with fuzzy color technique, which achieved an accuracy of 97.4% and 98.25%, respectively. Recently, [PERSON] et al. [32] proposed Choquet fuzzy classifier ensemble technique and [PERSON] et al. [33] proposed CNN models based on fuzzy rank and Gompertz function to detect coronavirus from CXR and CT images, respectively. [PERSON] et al. [34] utilized 15 distinct deep CNN models and [PERSON] et al. [35] suggested a DenseNet201 model based on three-class dataset for identification of COVID-19 patientswith an accuracy of 89.3% and 94%, respectively. When reviewing the related work, it was discovered that few researchers concentrated on fuzzy ensemble approaches paired with transfer learning models. An ensemble approach is a machine learning technique that integrates many base models to generate a single optimal prediction model. The outcomes of numerous models are integrated in order to enhance overall performance. Specifically, ensemble methods integrate numerous machine learning approaches into a single predictive model to enhance overall performance and predictions while reducing bias and variance. Furthermore, combining deep transfer learning models with fuzzy ensemble approaches will assist in increasing the accuracy and robustness of a detection system. The presented study considered the following. 1. CXR images were used to diagnose coronavirus, and a transfer learning and fuzzy ensemble fusion model was proposed for COVID-19 detection. 2. At first, we trained the VGG16, GoogleNet, and Xception base classifiers using deep transfer learning techniques. Using fully connected layers and the softmax layer, we extracted features and classified CXR images using pre-trained transfer learning models. 3. Then, using Majority Voting, Sugeno Integral, and Choquet fuzzy integral, the data prediction scores from multiple models were integrated to create the final forecasted labels that are more accurate than the separate model predictions. 4. For the application of Choquet fuzzy integral with other state-of-the-art models, such as Majority Voting and Sugeno Integral, we considered the validation accuracy of each classifier to compute the fuzzy membership values of each classifier. The advantage of this type of fusion is that it creates the final prediction of each sample using adaptive weights depending on each sample's confidence scores. 5.