Deep neural network for food image classification and nutrient identification: A systematic review.

Abstract

Technology impacts human life in both the aspects such as positive and negative, which helps in better communication and eliminating geographical boundaries. However, social media and mobile devices may lead to severe health conditions such as sleep problems, depression, obesity, etc. A systematic review is conducted to analyze health issues by tracking food intake by considering positive aspects using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Guidelines. The major scientific databases (such as Web of Science, Scopus, and IEEE explore) are explored to search the image recognition and analysis articles. The search query is applied to the databases using keywords like "Food Image," "Food Image Classification," "Nutrient Identification," "Nutrient Estimation," and using "Machine Learning," etc. 771 articles are extracted from these databases, and 56 are identified for final consideration after rigorous screening. A few investigations are extracted based on available food image datasets, hyperparameters tuning, a technique used, performance metrics, and challenges of Food Image Classification (FIC). This study discusses different investigations with their proposed FIC and nutrient estimation solution. Finally, this intensive research presents a case study using FIC and object detection techniques to estimate nutrition with food image analysis.