Revolutionizing Fast Food Classification: A Convolutional Neural Network Approach for Enhanced Nutrition Awareness

Nutrition is a fundamental requirement for human existence. In contemporary times, the populace exhibits a notable inclination toward fast food due to its taste and widespread popularity. Nevertheless, the consumption of fast food often comes with the drawback of elevated calorie, fat, and sodium content, posing potential health risks. Furthermore, discerning the precise composition of fast food can be challenging, given the lack of detailed ingredient disclosure. In light of these considerations, this study introduces a Convolutional Neural Network model designed to categorize fast food items. The model is extensively trained using an expanded fast food classification dataset encompassing ten distinct categories. Impressively, it attains an accuracy rate of 87.06% during training and 83.31% during validation. The model’s remarkable performance and capacity to generalize underscore its prospective influence on sectors like the food industry, public awareness, and nutritional investigation.