Estimation of food Calorie Detection: A Systematic Literature Review

Estimation of food Calorie Detection: A Systematic Literature Review

[1]Rohith H R,[2]Spanditha I S,[3]Srinidhi Desai,[4]Manjunath A G Mr.Nithin

(Assistant Professor) nnk@mcehassan.ac.in

Information and Science and Engineering, Malnad College of Engineering

Hassan-573202, India

Email id: hrrohith2003@gmail.com,spanditha1014@gmail.com,srinidhidesai75@gmail.com, manjunathag8787@gmail.com

Abstract— Food calorie discovery has surfaced as a vital area of exploration due to the global rise in rotundity and associated health pitfalls. This review synthesizes advancements across four studies that work deep literacy, computer vision, and convolutional neural networks( CNNs) for accurate food recognition and calorie estimation. ways include Inception V3, ResNet, TensorFlow APIs, andmulti-task CNNs, showcasing advancements in food image bracket, volume estimation, and calorie calculation. These styles address challenges like different food donations and dataset variability, paving the way for practical operations in salutary monitoring and healthcare. Keywords Food calorie discovery, convolutional neural networks, deep literacy, image recognition, TensorFlow,multi-task literacy, volume estimation.

The significance of this exploration lies in its capability to bridge gaps between theoretical advancements and practical operations, especially in the environment of global health challenges. By understanding the methodologies and results of these studies, this paper aims to give a consolidated resource for experimenters, masterminds, and interpreters in this arising sphere.

Keywords—Machine       learning,       Deep       learning,Face Recognition, Convolutional neural networks,