Developing a Multimodal Deep Learning System for Comprehensive Nutritional Analysis of Meals for Diabetes Management

Abstract

The management of diet is a pivotal factor in the maintenance of ideal blood glucose levels in individuals diagnosed with diabetes. Precisely evaluating the nutritional value of meals, encompassing caloric intake, can pose a formidable challenge. The present research suggests the creation and implementation of a multimodal deep learning framework aimed at approximating the nutritional composition of meals through the integration of image and textual information. The proposed system aims to combine convolutional neural networks (CNN) for image analysis with recurrent neural networks (RNN) or transformer models for text analysis. This integration is intended to exploit the complementary nature of visual and textual meal data, resulting in more precise estimates. The system is trained using a significant dataset consisting of images of meals, their corresponding textual descriptions, and related nutritional data. This dataset forms the foundation for the system’s development. The model’s predictive accuracy is evaluated through a rigorous assessment on unseen data, utilizing appropriate regression metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). In addition, we have created a proof-of-concept software application to showcase the practicality of the model in real-world scenarios. The objective of this application is to simplify the process of nutritional monitoring for individuals who have diabetes. The results of this study have the potential to revolutionize dietary management strategies in the context of diabetes care, as they provide a comprehensive and user-friendly nutritional analysis tool. Prospective areas of research encompass enhancing the precision of the model, expanding its scope of food items, and amalgamating it with other healthcare frameworks to achieve a comprehensive approach towards the management of diabetes. .