Optimizing Image Feature Extraction with Convolutional Neural Networks for Chicken Meat Detection Applications  
[PERSON]  
1 GDDP, Department Electronic Engineering, School of Engineering, University of Valencia, 46100 Burjassot, Valencia, Spain; [EMAIL_ADDRESS]  
[PERSON]  
2 Departamento de Desarrollo de Software, Poligono Industrial 'Fuente del Jarro'--Calle Villa de Bilbao, PYGSA Sistema y Aplicaciones, S.L., 10, 46988 Palerma, Valencia, Spain; [EMAIL_ADDRESS] (A.P.-A. ); [EMAIL_ADDRESS] (L.L.-S.)  
[PERSON]  
2 Departamento de Desarrollo de Software, Poligono Industrial 'Fuente del Jarro'--Calle Villa de Bilbao, PYGSA Sistema y Aplicaciones, S.L., 10, 46988 Palerma, Valencia, Spain; [EMAIL_ADDRESS] (A.P.-A. ); [EMAIL_ADDRESS] (L.L.-S.)  
[PERSON]  
1 GDDP, Department Electronic Engineering, School of Engineering, University of Valencia, 46100 Burjassot, Valencia, Spain; [EMAIL_ADDRESS]  
Footnote 1: email: {lorens-  
detection, accurate discrepancy identification, and continuous process optimization are key to achieving production excellence. A multitude of detection algorithms has been developed, leveraging a spectrum of image-processing techniques and integrating various signal-processing methodologies [1]. Nonetheless, manual approaches often encounter challenges in extracting features that comprehensively capture the nuanced characteristics of meat images. Consequently, machine learning techniques have emerged as effective alternatives for analyzing food images. A gamut of conventional machine learning algorithms is deployed in food data analysis, including support vector machines (SVMs) [2], K-means clustering, and artificial neural networks (ANNs) [3]. However, effectively leveraging these algorithms to exploit food architecture information is challenging due to the non-rigid and deformable nature of food items. As a result, traditional algorithms are limited in representing these intricate features. While segmentation techniques in medical imaging, such as those reviewed by [PERSON] and [PERSON] [4], [PERSON] et al. [5], and [PERSON] et al. [6], have been foundational for automating medical diagnosis, this work focuses on food image classification using convolutional neural networks (CNNs). Although these methodologies have been primarily used for segmentation tasks, CNNs are also effectively applied in image classification. In this context, CNNs have been widely used to classify food types, detect defects, and evaluate the quality of food products. While segmentation focuses on identifying specific regions within an image, our study explores the ability of CNNs to classify entire images, facilitating the identification of specific parts of products, such as chicken parts, with high precision and efficiency. The use of CNNs for food image classification has shown great potential for automating and improving quality control processes in the food industry. Automatic classification allows for not only rapid and accurate identification of chicken parts but also increased efficiency in production and processing, reducing human intervention and improving system reliability. The emergence of convolutional neural networks has garnered attention in scientific communities due to their aptitude for understanding complex patterns in images, which is pivotal for inspecting meat products. Highlighting the resemblance between the topology of CNNs and biological systems reinforces the effectiveness of CNNs in this context. CNNs operate by applying filters that detect specific features in different parts of the image. In the context of meat quality control, this implies that the network can identify characteristics such as color, texture, shape, and potential imperfections. By employing CNNs in meat quality control, significant benefits can be realized. For instance, defect detection can be more accurate and efficient compared to traditional methods. Furthermore, automating this process reduces the need for manual inspection, thereby enhancing the speed and consistency of evaluating meat quality.