A Real-time Food Image Recognition System to Predict the Calories by Using Intelligent Deep Learning Strategy

Abstract

Modern lifestyle emphasizes the significance of maintaining a daily healthy diet and ensuring a balanced intake of essential nutrients. The estimation of nutrient content within meals holds immense importance, particularly in addressing critical health issues like diabetes, obesity, and cardiovascular diseases. Among the crucial elements of a health-focused dietary plan is the calculation of calorie intake. In light of this, we propose a novel approach on deep learning techniques to accurately determine the calorie content of food items present within user-captured images by Using Intelligent Deep Learning Strategy. Our approach employs a layer-based methodology for calorie prediction within food items. The proposed system operates as a comprehensive food recognition solution that, given an adequate amount of relevant data, enables users to meticulously monitor their daily caloric intake. Users are able to submit images either from their image gallery or by utilizing their mobile phone cameras. Our primary objective is to not only identify the food category but also predict its calorie content. Notably, our model showcases exceptional performance with an accuracy of 0.98, signifying its capability to correctly classify 98% of instances within the dataset. This achievement underscores the efficacy of our approach in accurately estimating the calorie content of various food items, potentially serving as a valuable tool for individuals seeking to manage their dietary choices more effectively.