Abstract
Obesity is becoming an increasingly serious global health issue with severe consequences. Effective nutrition management is crucial in combating this epidemic. In Kazakhstan, traditional foods are a central part of the culture, yet comprehensive data and tools for analyzing dietary habits are lacking. Leveraging advances in computer vision, we developed a convolutional neural network (CNN) based approach to automatically classify images of traditional Kazakh dishes. We compiled a new dataset of 9,577 images across 22 categories of Kazakh foods and used it to train and evaluate several CNN models. The best-performing model (a fine-tuned DenseNet121) achieved a top-1 classification accuracy of 95%. These results highlight the potential of AI-based food recognition for dietary monitoring, nutritional assessment, and cultural preservation. Furthermore, the trained model was deployed in a Telegram chatbot to enable real-time food identification through image uploads, demonstrating a practical application of the system.