KAZAKH TRADITIONAL FOOD IMAGE CLASSIFICATION USING CNNS

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.

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