## 5 Results and Discussion
### Discussion  
When comparing the results with and without the use of color filters, as well as with and without the application of data augmentation techniques, significant improvements in the performance of different feature extraction methodologies applied to classes C1, C2, C3, and C4 can be observed. When evaluating convolutional neural networks and saliency maps, it is evident that CNNs are designed to learn hierarchical and complex features directly from raw images. Data augmentation helps generalize the network by exposing it to a greater diversity of examples. Saliency maps highlight the most important areas of an image, aiding CNNs in focusing on relevant regions, thereby enhancing the model's predictive accuracy. Histogram of Oriented Gradients and Local Binary Pattern capture different types of image information. HOG captures contour and shape information using gradients, performing well in specific scenarios, but it may be sensitive to variations introduced by data augmentation. Similarly, LBP captures texture patterns and can also be affected by augmentation-induced variations that do not align with the original textures. Data augmentation generates variations of training images, enabling CNNs to learn invariant and robust features against transformations like rotations, translations, and scalings. For saliency, augmentation can highlight consistent features across diverse conditions. However, augmentation techniques can introduce noise and unrepresentative features in images, particularly affecting methods like HOG and LBP, which rely on specific gradient and texture features. CNNs and saliency benefit from redundancy and noise handling capabilities inherent in deep learning frameworks, enabling them to maintain performance integrity even when dealing with noisy or augmented data. In contrast, HOG and LBP may struggle with noisy data, as it can degrade their effectiveness in feature extraction and classification tasks. Overall, the results suggest that the CNN is the most effective method for this classification task, particularly when combined with data augmentation and color filtering. The saliency method also shows promise, especially when used in conjunction with color filters. This comparison underscores the importance of leveraging advanced techniques such as CNN and saliency, alongside appropriate preprocessing and augmentation strategies, to achieve significant improvements in classification model performance. Analysis of Methods and Comparison with Other Studies  
The results obtained from our study are summarized in Table 13. We employed four methods to classify chicken meat: CNN, CNN + HOG, CNN + LBP, and CNN + saliency. The accuracies of each method across four classes (C1, C2, C3, and C4) are presented. To contextualize our findings, we compare these results with those reported in other studies focusing on similar classification tasks of meat and fish products, where the underlying algorithm may be a CNN but with potentially different parameter settings or feature combinations. In our study (Table 13), the CNN method achieved perfect accuracies of 100% across all four classes (C1, C2, C3, and C4). The CNN combined with HOG, LBP, and saliency achieved accuracies of 97.14%, 95.62%, 96.30%, and 91.92% for classes C1, C2, C3, and C4, respectively. Comparatively, in related studies (Table 14), [PERSON] et al. [57] reported 92.5% accuracy for red meat freshness classification using a CNN-based method. [PERSON] et al. [58] reported accuracies of 88.3%. Additionally, [PERSON] and [PERSON] [59] achieved 95.1%accuracy with a hybrid CNN-SVM approach for shrimp freshness diagnosis, and studies by [PERSON] et al. [60], [PERSON] et al. [61; 62], [PERSON] and [PERSON] [63], and [PERSON] et al. [64], accuracies of 91.7%, 89.6%, 93.2%, 90.4%, and 94.8%, respectively, for various classification tasks of meat and fish products. These values reflect the diversity in accuracy across different methods and datasets in the field.