## 2 Materials and Methods
### Deep Learning Models  
A convolutional neural network (CNN) is a sophisticated deep learning algorithm crafted for analyzing data with a grid-like topology, such as pixel data in images [34]. Distinct from standard neural networks, CNNs boast a unique structure that exploits the inherent spatial relationships within data. This is achieved through the deployment of convolutional filters across multiple layers, which are adept at autonomously identifying and learning from local patterns such as lines, textures, and contours. As one progresses through the layers of the network, these elementary features are synthesized into increasingly complex and significant conceptual depictions [34]. A key strength of these models lies in their innate capability to autonomously identify and learn salient features directly from raw data, eliminating the need for manual preprocessing steps [34]. As the training progresses, CNNs are designed to discern and prioritize the most promising features in a hierarchical manner. This intrinsic proficiency renders them exceptionally adept at a variety of computer vision applications [34; 35; 36]. Pre-trained CNN networks are models that have undergone training on extensive datasets such as ImageNet and are highly regarded within the deep learning domain [34; 35]. These networks are adept at identifying a diverse array of visual elements, making them an excellent groundwork for further computer vision research. Utilizing these pre-trained models allows one to capitalize on the knowledge they have amassed, thereby conserving considerable time and resources that would otherwise be spent on training from the ground up and facilitating the achievement of high-quality outcomes with greater efficiency [36]. In this study, ConvNeXt-T [37], EfficientNet v2 [38], ResNet50 [39], VGG16 [40], and AlexNet [41] are utilized for classification tasks. EfficientNetV2 [38] represents an advancement over its predecessor, EfficientNetV1, and introduces a novel class of convolutional neural networks [42]. This upgraded version prioritizes two key objectives: accelerating the training process and boosting the efficiency of parameters. It uses a smart combination of neural architecture search that considers training needs and compound scaling methods [42] to achieve these goals. ConvNeXt-T [37] represents a modernized convolutional network, evolving from traditional models such as Resnet through the integration of advanced methods akin to vision transformers. This evolution has established ConvNeXt-T as a favored instrument within the image-processing domain [43,44]. Distinguished from its counterparts, ConvNeXt-T is an architecture that stands out by employing convolution blocks organized into channel groups, which significantly enhances the network's ability to learn [43]. The ConvNeXt-T framework is composed of an array of convolution blocks, each featuring channel grouping [37]. These are succeeded by pooling and fully connected layers. The channel grouping feature refines the traditional convolution layers by segmenting the input channels into distinct groups, which then connect to only a portion of the output channels [43]. This design trims the network's parameter count and bolsters its ability to learn. ConvNeXt-T's contribution lies in its convolution blocks with channel grouping, coupled with a residual convolution block structure, which collectively enhances learning efficiency and mitigates the risk of overfitting [44]. Additionally, the architecture's multiple convolution blocks with channel grouping, followed by pooling and fully connected layers, contribute to its optimal performance in various computer vision applications [43,44]. As the CNN network deepens, there can be a decline in both performance and convergence speed [45]. ResNet50 [39], a type of residual network, addresses this issue by efficiently extracting features from input data through a series of stacked residual blocks.