Estimating glycemic index in a specific dataset: The case of Moroccan cuisine

Abstract A healthy lifestyle encompasses physical, mental, and emotional well-being, with healthcare and nutrition as central components. For those with chronic diseases such as diabetes, effective self-management involves continuous monitoring and dietary adjustments. Understanding the glycemic index (GI) is vital, as it indicates how carbohydrates affect blood sugar levels. Advancements in artificial intelligence have enhanced diabetes management through food image recognition systems, which identify food items and provide nutritional information, helping individuals track their dietary intake and GI consumption effectively. Despite their high performance, existing systems often lack inclusivity for diverse cuisines, such as Moroccan cuisine, which is known for its unique dishes of spices and health benefits. This study addresses these gaps by proposing the first comprehensive Moroccan food dataset, comprising 8,300 images across 70 food categories. The research subsequently proposes an advanced model to enhance food image recognition accuracy using convolutional neural network and attention mechanisms achieving more than 90% accuracy. In addition, estimating the GI values of Moroccan foods will help to raise public awareness of their health implications and facilitate decision-making on dietary self-management. The results demonstrate state-of-the-art performance, indicating promising potential for the first GI estimation of Moroccan food images.