## 5 Results and Discussion
In the classification of class C1, CNN, HOG, and LBP exhibited strong performance, with the CNN slightly ahead with an F1-score of 99.26%, and the saliency method also being effective, with an F1-score of 93.85%. In class C2, the CNN demonstrated perfect performance, while the LBP and HOG methods also performed well, with \begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{4}{c}{Class C4} \\ \hline Method & Sen. (\%) & Spe. (\%) & Pre. (\%) & F1-Score (\%) & Acc. (\%) \\ \hline LBP & 95.52 & 99.10 & 96.97 & 96.24 & 97.81 \\ HOG & 95.52 & 99.10 & 96.96 & 96.24 & 97.80 \\ CNN & 100.00 & 99.55 & 98.53 & 99.26 & 99.26 \\ Saliency & 97.01 & 100.00 & 100.00 & 98.48 & 98.89 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._F1-scores of around 97%. In class C3, the CNN achieved perfect scores, and the HOG and LBP methods also performed well, with F1-scores of around 98%. Lastly, in class C4, the CNN achieved perfect scores, and the HOG and LBP methods were close, with F1-scores of around 96%. The saliency method performed well with an F1-score of 98.48%. This analysis indicates that the CNN consistently achieved superior results compared to other feature extraction methods across all classes, even when color filters were used but data augmentation techniques were not applied. Tables 11 and 12 present the results of the CNN tests applied to different feature extraction methods for classes C1 and C2 in Table 11 and for classes C3 and C4 in Table 12. In this analysis, color filters were used and data augmentation techniques were applied. In the classification of class C1, the CNN maintained perfect scores, and the saliency method also performed well, with an F1-score of 98.53%, while LBP struggled with a recall of 64.71%. For class C2, the CNN exhibited perfect performance, and the saliency method followed closely with an F1-score of 96.34%, while LBP had the lowest recall of 61.45%. In class C3, the CNN achieved perfect scores, and the saliency method performed well, with an F1-score of 98.61%, while LBP had a recall of 100% but a lower precision of 75%. Lastly, in class C4, the CNN achieved perfect scores, and the saliency method followed with an F1-score of 95.59%, while LBP had a low recall of 89.55%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{Class C1} \\ \hline Method & Sen. (\%) & Spe. (\%) & Pre. (\%) & F1-Score (\%) & Acc. (\%) \\ \hline LBP & 64.71 & 100.00 & 100.00 & 78.57 & 82.35 \\ HOG & 98.53 & 97.75 & 93.06 & 95.71 & 97.14 \\ CNN & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Saliency & 98.53 & 99.55 & 98.53 & 98.53 & 99.04 \\ \hline \hline \multicolumn{5}{c}{Class C2} \\ \hline Method & Sen. (\%) & Spe. (\%) & Pre. (\%) & F1-Score (\%) & Acc. (\%) \\ \hline LBP & 61.45 & 96.62 & 87.93 & 72.34 & 79.03 \\ HOG & 98.80 & 94.69 & 88.17 & 93.18 & 95.62 \\ CNN & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Saliency & 95.18 & 99.03 & 97.53 & 96.34 & 97.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: CNN test results for classes C1 and C2 using color filters and data augmentation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{Class C3} \\ \hline Method & Sen. (\%) & Spe. (\%) & Pre. (\%) & F1-Score (\%) & Acc. (\%) \\ \hline LBP & 100.00 & 88.99 & 75.00 & 85.71 & 92.65 \\ HOG & 93.06 & 99.54 & 98.53 & 95.71 & 96.30 \\ CNN & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Saliency & 98.61 & 99.54 & 98.61 & 98.61 & 99.08 \\ \hline \hline \multicolumn{5}{c}{Class C4} \\ \hline Method & Sen. (\%) & Spe. (\%) & Pre. (\%) & F1-Score (\%) & Acc. (\%) \\ \hline LBP & 89.55 & 85.65 & 65.22 & 75.47 & 79.03 \\ HOG & 83.58 & 99.55 & 98.25 & 90.32 & 91.92 \\ CNN & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Saliency & 97.01 & 98.21 & 94.20 & 95.59 & 96.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: CNN test results for classes C3 and C4 using color filters and data augmentation.