## 2 Related Work
DL automatically derives features from text and images, achieving the most precise representation of characteristics through a layer-by-layer translation process [27]. A CNN is a DL model that analyzes visual inputs, including images, animations, and videos [10]. It comprises various layers executing different functions, such as the convolution, pooling, fully connected, and dropout layers. CNNs also include activation functions like sigmoid, ReLU, and SoftMax, which have varying ranges [28; 29]. Frequently employed in the deep learning framework, CNNs are used in various real-time applications and tasks, such as NLP, computer vision, and image classification. Moreover, CNN is a type of multilayer neural network that stands out in the field of computer vision. Designed to analyze visual features from image patches with minimal or no preprocessing, CNNs have significantly outperformed other models in recent years [30]. A CNN is composed of input, output, and hidden layers. This model predicts likely labels based on datasets provided for training and testing purposes. CNNs can utilize any form of data due to their ability to circumvent complexity constraints, further reinforcing their superiority [30]. CNN models are extensively employed for image classification, identification, object recognition, and image captioning [31]. They use various convolution operations on images to capture data characteristics. CNNs efficiently represent and organize image data, rapidly collecting visual features from extensive datasets [22]. They can address nonlinear challenges and complex regression by utilizing sparse connections, spatial subsampling, and weight-sharing [32]. CNN architectures include components like activation functions, convolutional layers, fully connected, hidden, pooling, and normalization layers. Additional layers can be implemented for simulations requiring greater complexity. CNNs have revolutionized image classification [22]. Feature maps can be generated by applying multiple filters in the convolution layer, which are then processed with a nonlinear activation function. While conventional CNNs are effective for traditional image classification, hyperspectral image recognition necessitates the practical application of both spatial and spectral associations. CNNs are distinguished by their ability to identify essential features without human intervention. AlexNet, ResNet, and VGG are among the CNN models successfully implemented and studied [33]. AlexNet is the most influential CNN, is extensively applied to image classification, and won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012 [30]. It is equipped with numerous pre-trained DCNNs. The most notable features of AlexNet are the additional filters in each layer, the pooling layer that follows each convolutional layer, and the use of ReLU to introduce nonlinearity, which increases speed by up to 6\(\times\) while maintaining the same level of accuracy [34]. It employs an overlapping pooling layer to minimize the network's size and uses a dropout layer as a form of regularization to address overfitting [34]. The utilization of AlexNet in this study for multiple feature classifications was motivated by these characteristics. The Visual Geometry Group (VGG) is a deep CNN framework. The term \"deep\" indicates the number of layers in the network, with VGG-16 having 16 convolutional layers and VGG-19 having 19 convolutional layers [35]. This framework serves as the foundation for advanced object detection methods. Beyond its success on ImageNet, VGG surpasses baseline models on various datasets and is recognized as one of the most widely used image classification architectures today [22]. The VGG Network, based on the CNN paradigm and utilizing multilayer functions, was developed collaboratively by the Visual Geometry Group at Oxford University and Google DeepMind researchers. It is widely used in various applications, such as healthcare, due to its high performance and simple structure.