## 3 Results
### Experimental Setup  
Given the dataset's modest size and the structured nature of the data, the 10-fold cross-validation technique was employed. This resampling strategy is instrumental in assessing a model's performance and its ability to generalize. It involves dividing the dataset into 10 equally sized segments, known as folds. The training occurs on 9 folds, while the 10 th fold is used for testing. This cycle is conducted 10 times, ensuring each fold is used once as the test set. Such a method is effective in reducing overfitting and yields a more dependable performance metric for the model on new data. Additionally, the ratio of class imbalance was preserved across each fold. Additionally, min-max normalization was applied to the tabular data to observe whether it would have an effect on the results. Min-max normalization, commonly referred to as feature scaling, is a method that adjusts the values of a feature to fall within a designated range, often between 0 and 1. The transformation is defined as follows:  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{9}\]  
where \(x\) is an original value, min (\(x\)) is the minimum value of the feature, max (\(x\)) is the maximum value of the feature, and \(x^{\prime}\) is the normalized new value. This normalization process ensures that the values of the feature are scaled proportionally within the specified range, preserving the relationships among the original data while enabling more effective comparison and processing by machine learning algorithms. During the training phase of the deep learning algorithms, both transfer learning and training from scratch were applied. The stopping condition was set to 60 epochs or a training loss of less than 0.01. Stochastic gradient descent (SGD) [49] was utilized in all deep learning models. ConvNeXt-T was trained with adaptive moment estimation with weight decay regularization (AdamW) [50] because it is a newer model compared to the others. A batch size of 4 was used in all deep learning architectures. In the hybrid model, the RF and ResNet50 models, which had the highest accuracy among canonical machine learning and deep learning models, were utilized. When selecting deep learning models, those utilizing SGD for optimization were considered. In the RF-ResNet50 hybrid model, features automatically extracted from crayfish images were utilized to feed the RF algorithm. The features were obtained from the average pooling layer immediately preceding the fully connected layer of the ResNet50 model. By providing these features as input to the RF algorithm, the class of the image was determined. The architecture of the hybrid model is given in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{CM_{2}}\) **(1)** & \(\mathbf{CM_{2}}\) **(0)** \\ \hline \(\mathbf{CM_{1}}\) (1) & \(n_{11}\) & \(n_{10}\) \\ \hline \(\mathbf{CM_{1}}\) (0) & \(n_{01}\) & \(n_{00}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Contingency table. According to Table 2, the best accuracy, sensitivity, precision, F1-score, and MCC performances were obtained with 0.661, 0.404, 0.655, 0.5, and 0.282, respectively, by utilizing the RF model. On the other hand, the best specificity performance was obtained with 0.862 by utilizing the NB model. It can be said that the imbalanced data led to poor performance in detecting sick individuals. According to Table 3, the best accuracy, precision, and MCC performances were obtained with 0.643, 0.684, and 0.242, respectively, by utilizing the RF model. The best results for sensitivity and F1-score were obtained with 0.447 and 0.494, respectively, by utilizing the SVM model. Finally, the best specificity performance was obtained with 0.985 by utilizing the NB model. All models, except SVM, experienced a performance loss in accuracy and sensitivity evaluation metrics. Therefore, it can be said that the data normalization had no influence on the performances of the canonical machine learning classifiers for the dataset.