Food Detection and Calorie Estimation using Deep Learning

Abstract

Abstract: For the fight against obesity, precise food and energy intake measurement techniques are essential. One of the most important lessons for long-term prevention and effective treatment programmes is the provision of users and patients with practical and intelligent solutions that assist them in measuring their food intake and gathering dietary information. In this article, we suggest a calorie measurement technique to assist patients and medical professionals in their battle against dietrelated illnesses. In this document, we suggest a food identification system that, when given the appropriate quantity of data, can assist a user in keeping track of daily caloric consumption. Calorie estimation for the current method must be done by hand. The proposed model will use a deep learning algorithm to offer a special method of calculating calories. In the world of medicine, calorie calculations for food are crucial. because the calories in this food are beneficial to your health. This measurement is derived from photographs of various foods, including fruits and vegetables. Our suggested solution relies on cell phones, which enable the user to take a picture of the food and instantly calculate the number of calories consumed. We classify food photos for system training using deep convolutional neural networks to reliably identify the food in the system. In this study, we use a convolutional neural network (CNN) to detect and identify images of food. Given the huge range of food types, picture recognition of food products is frequently very difficult. Whatever the case, deep learning has recently been shown to be an incredibly innovative image identification approach, and CNN is the greatest way to use deep learning.