Deep Learning-based Food Calorie Estimation Method in Dietary Assessment: An Advanced Approach using Convolutional Neural Networks

Abstract

Dietary pattern assessments, essential for chronic illness management and well-being, involve time-consuming manual data input and food intake remembering. A more dependable and automated approach is needed since such procedures may create mistakes and inconsistencies. This study solves a long-standing problem by automating nutritional assessment using deep learning and image analysis. CNNs, deep learning models for image processing, were employed in our study. Food category algorithms are trained with thousands of pictures. Even with numerous food items, these models can distinguish them in digital photographs. Our method calculates food portions after identification. Photometric food measurements are obtained using reference objects like plates and forks. Yet another deep learning model predicts portions. The method evaluates food calories last. Select food types and portions are matched to nutritional databases. These findings might automate, enhance, and user-centrically assess food intake in health informatics. Our first experiments are encouraging, but we must understand the approach's limits and need for refinement. The findings underpin future research and development. This approach envisions a future where patients can monitor their nutrition and doctors can get accurate data. This may prevent and treat lifestyle problems.