## 2 Preliminaries  
**VGG16:** VGG16 was proposed by [PERSON] et al. [36] in 2014, which was termed based on its 16 layers. VGG-16 contains different layers, such as three dense layers, two FL, five max-pooling layers, and 13 convolutional layers. **GoogleNet:** With a reported performance of 93.3%, GoogleNet [37] succeeds at the leading edge of the ImageNet detection and classification problem. The greater utilization of computer resources in the model is its advantage, in which a construction component is treated as an \"inception module\" that reflects the model's increased width and depth. This was one of the earliest CNN designs that diverge from the traditional method of just adding convolution and pooling layers in a sequential manner. Figure 1 illustrates the GoogleNet architecture, which is composed of nine initialization modules. GoogleNet is a 22 deep CNN that is a version of Google researchers' Inception Network that is built on the Inception architecture. In this case, we utilized the third version of the GoogleNet model, Inception V3. This version is made up of a hierarchy of sophisticated inception modules that integrate channel spatial convolution, pooling, and reprojection operations at different scales in each module. The model shrinks the parameter space by breaking spatial convolutions with bigger filter sizes n \(\times\) n into a series of two convolutional operations with n \(\times\) 1 and 1 \(\times\) n filter sizes. The resultant network model is more complicated and deeper than AlexNet, although it has fewer features and lower computing complexity than AlexNet. GoogleNet does not employ fully-connected layers. Instead, the final convolutional map is subjected to channel-wise average activation values, and global average pooling values of the 2048 channels are utilized as the input image's feature vector. **Xception:** The Xception [38] architecture includes a depth-wise separable convolution layer, convolutional layer, residual connections, and inception module. The design of Xception, which stands for \"extreme inception\", has 36 convolution layers that form the network's extractor base. Xception is an extension of Inception architecture by using deeply separable convolutions for standard Inception modules and, thus, has improved speed and accuracy compared to Inception, ImageNet, Inception V3, ResNet-101, ResNet-50, and VGGNet models. The Xception model contains three major sections: entry, middle, and exit flow. The data first enter the input flow, and then go through the eight-fold middle flow, and finally the exit flow. Batch normalization is accompanied by all the Convolution layers. Xception replaced the conventional starting block by expanding it and changing the various (1 \(\times\) 1, 5 \(\times\) 5, 3 \(\times\) 3) spatial dimensions with a (3 \(\times\) 3) single dimension preceded by a (1 \(\times\) 1) transition to maintain computational burden. Equation(1) and Equation(2) represent the mathematical modelling of Xception:  
\[f_{l+1}^{k}(p,q)=\sum_{x,y}f_{l}^{k}(x,\ y).\ \mathrm{e}_{l}^{k}(u,\ \mathrm{v}) \tag{1}\]  
\[F_{l+2}^{k}=g_{c}\left(F_{l+2}^{k},\ k_{l+1}\right) \tag{2}\]  
**Choquet fuzzy:** In this study, aggregation refers to the process of merging each classifier's performance scores into a single global score. For the said purpose, an aggregation operator was used. Normally, we consider weighted average as the aggregation operator; however, in this situation, we employed the Choquet fuzzy integral [39]. This approach has already been performed in a variety of pattern identification applications due to its benefit of capturing the unpredictability that exists in the decision values obtained as extra data via classifier fusion, which is lacking in traditional ensemble approaches. As a result, fuzzy measures are the extension of aggregation operations on a set of confidence values. Weighted values are assigned to distinct classifier combinations in these fuzzy measures.