# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 1 Introduction  
Celiac disease is an immune-mediated disease triggered by gluten consumption in genetically susceptible individuals [1]. Gluten is a protein complex found in wheat, rye, and barley [2]. Celiac disease is estimated to affect 0.6% to 1.0% of the world's population [3]. Gluten consumption for people with celiac disease carries significant health risks, primarily due to the malabsorption of nutrients and persistent damage to the intestines [4]. Multiple organ systems may be affected, resulting in deficiencies in vitamins and minerals, such as calcium, iron, and vitamin D [5]. This can lead to conditions, such as anemia, osteopenia, fractures, and reduced bone mineral density (BMD) [6] and an increased risk of cancer [7]. Celiac disease patients must adhere to a strict gluten-free diet throughout their lives, which is currently the only effective treatment [8]. As part of the gluten-free diet, celiac patients must pay particular attention to the food products that they consume, checking the ingredients to ensure that they do not contain gluten, as even small traces of gluten can have serious health consequences [9]. This may be a daily challenge, especially when eating out. Individuals with celiac disease should ensure their food is entirely gluten-free when dining in social settings, such as family events, workplace gatherings, and restaurants. Today, many restaurants offer gluten-free menu options, making it easier for individuals with celiac disease to consume gluten-free food outside their homes. However, dining out may pose a risk for individuals with celiac disease, since they must depend on restaurant staff to ensure that their meals are gluten-free. In some cases, unintentional errors can occur, making it difficult to guarantee with complete certainty that a food is gluten-free. The restaurant staff may not be aware that certain ingredients contain gluten, leading to accidental exposure to gluten. There have been cases in which severe allergic reactions have led to tragic outcomes due to unintentional mistakes made by restaurants. In one instance, a person allergic to sesame tragically died after being served a dish containing sesame despite informing the restaurant of his allergy [10]. Furthermore, a young girl with a life-threatening dairy allergy died after being exposed to cow milk, despite her mother's instructions to avoid cow milk [11]. Consequently, celiac patients may feel anxious and uncertain about eating outside the home. The availability of accurate and reliable tools that aid in verifying the presence of gluten in food could greatly enhance their sense of safety and confidence when dining in social situations. Currently, devices like NimaTM[12] can be used to test small samples of food for gluten, but these tools come with several limitations. Each test requires a disposable capsule, making the sensor an expensive item to purchase and maintain. Furthermore, celiac disease patients must remember to carry the sensor and its capsules. The reliability of the test devices may vary depending on the food and the user technique [13]. The sensor itself only analyzes a small portion of the food, which may not fully represent the meal, leading to gluten that can be missed in other parts of the dish. Additionally, NimaTM detects gluten only when its concentration exceeds a legal threshold, meaning it may fail to identify trace amounts below this limit [14]. Recent advancements in food safety technology leverage machine learning and computer vision to enhance food monitoring, authentication, and contamination detection [15; 16]. Traditional gluten detection methods, such as NimaTM sensors, rely on chemical testing, which can be costly, time-consuming, and prone to errors due to sample size limitations. In contrast, computer vision-based approaches offer a scalable, cost-effective, and non-invasive alternative for food analysis, reducing reliance on specialized equipment and enabling real-time decision making.