KediNet: A Hybrid Deep Learning Architecture for Thai Dessert Recognition

Obesity and diabetes represent escalating health challenges, exacerbated by the widespread availability and convenience of processed foods. Automated dietary monitoring technologies offer valuable support to individuals managing these conditions by helping them track and regulate their consumption, particularly of high-calorie, sugar-rich foods. Toward this goal, this paper presents KediNet, a hybrid deep learning architecture that combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViTs) for Thai dessert recognition. Our main contributions include: 1) the proposal of KediNet, which outperforms nine state-of-the-art deep learning models; 2) a comprehensive comparison of classification performance across different model architectures, image resolutions, and data augmentation techniques; and 3) the creation of THDESS-20, a new publicly available dataset containing 5,242 images across 20 classes of Thai desserts. Experimental results demonstrate that KediNet achieves 98.64% balanced accuracy at $224\times 224$ resolution, surpassing all other tested models even at higher resolutions, while maintaining inference times suitable for mobile applications. Our analysis shows that KediNet effectively extracts meaningful features from Thai dessert images despite their high intra-class variation and visual similarities between classes. This work advances the field of food recognition for health applications while addressing the specific challenges of ethnic cuisine classification.