## 3 Experimental Results and Discussion
### Results and Discussion of Deep Learning Models
For our proposed model, we list the confusion matrix of the first fold of five-fold cross-validation for 11 classes in Figure 7 and the complete confusion matrix and its lower right part for clarity for 21 classes in Figure 8a,b (when zoomed in, the figure will be easy to read). The diagonal cells show the number of cases that were correctly classified, and the off-diagonal cells show the misclassified cases. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Time** & **Ratio** \\ \hline Our model & 1.31 & 1.00 \\ AlexNet 2 & 12.71 & 9.66 \\ GoogleNet 1 & 3.99 & 3.04 \\ GoogleNet 2 & 19.62 & 14.92 \\ Xception 1 & 11.78 & 8.96 \\ Xception 2 & 664.21 & 505.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The training time (in minutes) and the ratio of four deep learning models for 11 classes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Time** & **Ratio** \\ \hline Our model & 2.73 & 1.00 \\ AlexNet 2 & 29.39 & 10.78 \\ GoogleNet 1 & 8.10 & 2.97 \\ GoogleNet 2 & 42.36 & 15.53 \\ Xception 1 & 24.47 & 8.98 \\ Xception 2 & 1352.69 & 496.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The training time (in minutes) and the ratio of four deep learning models for 21 classes. Figure 7: The confusion matrix of the first fold of the five-fold cross-validation of our proposed model for 11 classes. Similar to Table 5 for SVMs, the worst sensitivity or recall (79.5%) among 11 classes occur in the last but one class, Class 10, and the worst precision (84.1%) also occurs in the last but one class. Therefore, the worst classification rate also occurs in the last but one class. Relatively, lower Hurst exponents or rougher images are easier to differentiate; the boundary lies in the middle class, Class 6 (\(H=1/2\)). The worst sensitivity or recall (49.0%) among 21 classes occurs in the last to one class, Class 20; likewise, the worst precision (58.3%) also occurs in the last but one class; therefore,  
Figure 8: The complete confusion matrix (**a**) and its lower right part (**b**) for clarity of the first fold of the five-fold cross-validation of our proposed model for 21 classes. the worst classification rate also occurs in the last but one class. Relatively, higher Hurst exponents or smoother images are more difficult to differentiate because their hidden features get lesser. The boundary lies about in the middle class, Class 11; that is, the classes of Hurst exponents lower than 0.5 can be correctly classified. Generally, the classification rate gets lower as the Hurst exponent gets larger. As a comparison, we also list the confusion matrix of the first fold of the five-fold cross-validation of GoogleNet 1 for 11 classes in Figure 9 and the complete confusion matrix and its lower right part for clarity for 21 classes in Figure 10a,b (when zoomed in, the figure will be easy to read). The worst sensitivity or recall (74.0%) among 11 classes occur in the last but one class, Class 10; likewise, the worst precision (76.7%) also occurs in the last but one class; therefore, the worst classification rate occurs in the last but one class. Likewise, lower Hurst exponents or rougher images are easier to differentiate; the diagonal cells show smaller numbers as the Hurst exponent gets larger. The worst sensitivity or recall (55.0%) among 21 classes occur in the last but one class, Class 20; likewise, the worst precision (57.0%) also occurs in the last but one class; therefore, the worst classification occurs in the last but one class. Similarly, higher Hurst exponents or smoother images are more difficult to differentiate. In general, the classification rate gets lower as the Hurst exponent increases. Figure 9: The confusion matrix of the first fold of the five-fold cross-validation of GoogleNet 1 for 11 classes.