## 6 Conclusions  
In conclusion, our study provides a comprehensive comparative evaluation of various image analysis methods for chicken meat classification. Our strategy began with the application of color filtering, which played a crucial role in enhancing the separation and highlighting specific features in images of chicken meat. This technique was pivotal in improving the differentiation among various types of chicken meat in our dataset. Subsequently, we employed feature extraction methods, including LBP, HOG, and saliency, to generate meaningful representations of the images, which served as inputs to the classifier. These methods were chosen for their efficiency in capturing relevant features, adapting well to the complexity and diversity of the images of chicken meat used in our study. Despite the significant advances achieved with the LBP, HOG, and saliency methods, it is important to note that while some of these methods achieved acceptable accuracy, none surpassed the precision achieved by the CNN method in our study. The CNN achieved a perfect accuracy of 100% in classifying the four types of chicken meat evaluated. The robustness and accuracy of the CNN were remarkable, establishing it as the most effective method for this task. It is noteworthy that the saliency method also proved to be highly effective, achieving accuracy values exceeding 94% and significantly contributing to the overall classification accuracy. Additionally, combining color filtering with the CNN optimized the precise identification of chicken parts with distinct chromatic characteristics. Moreover, the combination of color filtering with convolutional neural networks (CNNs) significantly optimized the identification of chicken parts with distinct chromatic features. This approach allowed for a reduction in the number of classes processed during model training. By reducing the number of classes from four to two in each model, the classifier was able to focus more on key features, leading to precise detection and demonstrating the effectiveness of combining image preprocessing with the power of CNNs. The integration of image processing, feature extraction, and CNNs can play a key role in industrial and medical applications. Instead of processing the entire image, feature extraction enables focusing on relevant characteristics, greatly improving execution time, which represents a critical factor in industrial settings. This facilitates faster and more accurate classification, essential in food quality control, where speed and accuracy are vital to ensure safe products. In the medical field, this optimization can also improve response times in emergency situations, expanding the potential for the rapid detection of critical conditions, such as sudden cardiac arrest. In the future, the combination of speed and accuracy in image classification will be key to optimizing more complex detection systems. With the continuous improvement of CNN architectures, such as AlexNet, VGGNet, InceptionV3, and MobileNet, along with advanced feature extraction techniques, food classification and medical diagnosis tasks can be addressed more efficiently. This could open up new possibilities for applications in industries such as food and healthcare, where both speed in classification and precision are essential to ensure safety and quality. Conceptualization, A.M., and A.R.-M.; methodology, A.M. and A.R.-M.; software, A.M.; investigation, A.M. and A.R.-M.; writing original draft preparation, A.M. and A.R.-M.; writing--review and editing, A.M. and A.R.-M.; Final review and approval: A.M., A.R.-M.,L.L.-S., and A.P.-A. ; Data provision, A.M., L.L.-S., and A.P.-A. All authors have read and agreed to the published version of the manuscript. This research received no external funding  
Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Authors [PERSON], [PERSON] and [PERSON] were employed by the company PYGSA Sistema y Aplicaciones, S.L.