Food Identification and Calorie Estimation System Using Deep Learning

In recent years, the widespread availability of smartphones and the growing interest in health and nutrition have spurred the development of applications aiming to assist individuals in managing their dietary intake. Among these, the identification and calorie estimation of consumed foods remain challenging tasks due to the inherent variability in food appearance and composition. This paper proposes a novel approach leveraging deep learning techniques to address these challenges effectively. The proposed system comprises two main components: food identification and calorie estimation. The food identification module utilizes convolutional neural networks (CNNs) trained on a large dataset of food images to accurately classify the type of food present in an image. This component employs transfer learning techniques to adapt pre-trained models to the specific task of food recognition, achieving high accuracy even in the presence of diverse cuisines and presentation styles. Following food identification, the system employs a calorie estimation module based on a combination of deep learning and food composition databases. This module utilizes recurrent neural networks (RNNs) to process textual descriptions of food items and their quantities, extracting relevant features for calorie estimation. Additionally, it incorporates information from established food composition databases to enhance accuracy and accommodate variations in portion sizes and ingredients.