## 5 Results and Discussion

Table 2: Execution time analysis of image processing (IP Time), training, and prediction for different methods. IP Time refers to the image-processing time (s). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** \\ \hline C1 & 52 (76.47\%) & 4 (5.88\%) & 10 (14.71\%) & 2 (2.94\%) \\ C2 & 6 (7.23\%) & 73 (87.95\%) & 2 (2.41\%) & 2 (2.41\%) \\ C3 & 0 (0\%) & 2 (2.78\%) & 68 (94.44\%) & 2 (2.78\%) \\ C4 & 0 (0\%) & 2 (2.99\%) & 0 (0\%) & 65 (97.01\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix without a color filter and without data augmentation. 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. This study evaluated the performance of four methods--CNN, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and saliency--for classifying images of chicken pieces from the PYGSA database. Experiments were conducted with and without a color filter and with and without data augmentation across four classes: C1, C2, C3, and C4. Results were measured in terms of recall, specificity, precision, and F1-score. Tables 5 and 6 present the results of the CNN tests applied to different feature extraction methods. It is noteworthy that, in this analysis, neither color filters nor data augmentation techniques were used. In the classification of class C1, the CNN achieved the best performance with a recall of 94.12% and an F1-score of 96.24%, while the HOG method exhibited the worst performance with a recall of only 27.94%. For class C2, the CNN again outperformed the other methods, achieving a recall of 98.80% and an F1-score of 97.62%. The LBP method showed reasonable performance with a recall of 85.54%, whereas the HOG method had a low precision of 55.88%. In class C3, the CNN achieved perfect scores across all metrics, while the LBP method also performed well with a recall of 90.28%. However, the HOG method had a low recall of 30.98%. Lastly, in class C4, the CNN showed exceptional results with a recall of 100%. The LBP method achieved a good recall of 91.04%, whereas the HOG method had a lower recall of 77.27%. This analysis demonstrates that the CNN consistently outperformed the other feature extraction methods across all evaluated classes, even without the use of color filters or data augmentation techniques. Tables 7 and 8 present the results of the CNN tests applied to different feature extraction methods for classes C1 and C2 in Table 7 and for classes C3 and C4 in Table 8. In this analysis, color filters were not used but data augmentation techniques were applied. In the classification of class C1, the CNN achieved perfect scores, while the saliency method also performed well, with an F1-score of 80.92%. The LBP method performed poorly with a 0% recall. In class C2, the CNN again achieved perfect scores, and the saliency method followed, with an F1-score of 85.37%, whereas the LBP method had the lowest recall of 26.51%. In class C3, the CNN maintained perfect scores, and the saliency method performed well, with an F1-score of 90.07%, while the LBP method had a low recall of 47.22%. Lastly, in class C4, the CNN achieved perfect scores, and the saliency method performed well, with an F1-score of 97.01%, while the LBP method had the lowest recall of 49.25%. This analysis indicates that the CNN consistently achieved superior results compared to other feature extraction methods across all classes, even when color filters were not used but data augmentation techniques were applied. Tables 9 and 10 present the results of the CNN tests applied to different feature extraction methods. In this analysis, color filters were used but data augmentation techniques were not applied.