Food-Type Recognition and Estimation of Calories Using Neural Network

Abstract

In the fast-moving world, obesity has become a major health issue to the human beings. BMI defines the obesity when it is greater than 30 kg/m². Obesity leads to many diseases like high cholesterol, liver failure, knee problems, diabetes, and sometimes cancer. When the patient eats healthy food, the obesity can be controlled. The obesity problem can be addressed when there is a system that monitors the food consumed by the patient automatically and gives the suggestion periodically to the patient in treatment of obesity. Many of the people find difficulty in monitoring their food intake periodically, due to less knowledge in nutrition and self-control. In this chapter, identification of food type is made and estimation of calorie is done using MLP and proposes the results. Single food item types were considered previously, but here mixed food item types are considered. Region of Interest (ROI) is used to identify the mixed food item type. The next step includes feature extraction process. The extracted feature image is fed into MLP classification to classify the food image. The volume of the food is used to calculate the calories present in the food. The implementation is processed in MATLAB with 1000 fruit images containing 6 food classes with good accuracy. The automatic dietary control is made available for the diabetic patients.