## 4 Experimental Results and Analysis  
Our proposed model was evaluated against widely utilized classification networks, including DenseNet, a SimpleCNN, and an MLP (Multi-Layer Perceptron). DenseNet, or Densely Connected Convolutional Networks, represents a novel deep learning architecture that enhances network efficiency through the establishment of dense interconnections among layers. In this architecture, each layer is explicitly connected to the outputs of all preceding layers, meaning that each layer's input consists of feature maps from all prior layers. This configuration significantly facilitates gradient flow, thereby mitigating the vanishing gradient issue commonly encountered in deep networks. The architecture of DenseNet promotes efficient feature reuse, which not only reduces the total number of parameters but also improves computational efficiency. Furthermore, DenseNet enhances the transmission and utilization of local features, which can markedly elevate the network's performance in tasks such as image classification and object detection. The DenseNet utilized in this research is set up with a growth rate of 32, block counts of [6; 12; 15; 22], and is designed for two output categories. The architecture of the SimpleCNN is derived from the Convolutional Neural Network (CNN) framework introduced by [PERSON] et al. in their research [24]. This network exemplifies a conventional CNN structure, comprising multiple convolutional layers, pooling layers, fully connected layers, and dropout layers. The input image is characterized by a dimension of 9 \(\times\) 9 pixels with N channels. The network is systematically organized into several segments: the initial segment features a 3 \(\times\) 3 convolutional layer equipped with zero padding and ReLU activation, succeeded by a 2 \(\times\) 2 max-pooling layer. The subsequent segment mirrors this configuration, incorporating another 3 \(\times\) 3 convolutional layer with zero padding and ReLU activation, and a 2 \(\times\) 2 max-pooling layer. The third segment continues this pattern, including a 3 \(\times\) 3 convolutional layer with zero padding and ReLU activation, and a 2 \(\times\) 2 max-pooling layer, followed by a dropout layer with a dropout rate of 0.25. The concluding segment consists of a fully connected layer utilizing ReLU activation, followed by an additional dropout layer with a dropout rate of 0.5. The final output is produced through a Sigmoid activation function, facilitating binary classification. The framework utilized in this research aligns with the SimpleCNN architecture mentioned earlier. The Multi-Layer Perceptron (MLP) represents a fundamental architecture in feedforward neural networks, characterized by the presence of multiple fully connected layers of neurons. The MLP comprises an input layer, two hidden layers, and an output layer. Neurons within each layer are interconnected with neurons from the preceding layer, establishing a fully connected network structure. Each connection is associated with a weightparameter, and neurons employ an activation function (such as ReLU, Sigmoid, or Tanh) to execute nonlinear transformations of the input signals. The training of MLPs is conducted through the backpropagation algorithm, which aims to optimize the weights and minimize the loss function, thereby enhancing prediction accuracy. MLPs are adept at managing complex nonlinear relationships, which accounts for their extensive application in various domains, including image recognition, speech processing, and natural language processing. The MLP utilized in this research features 128 neurons in the first hidden layer and 64 neurons in the second hidden layer. Given that the task involves binary classification, the output layer consists of one neuron.