Enhancing food recognition accuracy using hybrid transformer models and image preprocessing techniques

This study presents a robust approach for continuous food recognition essential for nutritional research, leveraging advanced computer vision techniques. The proposed method integrates Mutually Guided Image Filtering (MuGIF) to enhance dataset quality and minimize noise, followed by feature extraction using the Visual Geometry Group (VGG) architecture for intricate visual analysis. A hybrid transformer model, combining Vision Transformer and Swin Transformer variants, is introduced to capitalize on their complementary strengths. Hyperparameter optimization is performed using the Improved Discrete Bat Algorithm (IDBA), resulting in a highly accurate and efficient classification system. Experimental results highlight the superior performance of the proposed model, achieving a classification accuracy of 99.83%, significantly outperforming existing methods. This study underscores the potential of hybrid transformer architectures and advanced preprocessing techniques in advancing food recognition systems, offering enhanced accuracy and efficiency for practical applications in dietary monitoring and personalized nutrition recommendations.