Qaraqe, MarwaUsman, MuhammadAhmad, KashifSohail, AmirBoyaci, Ali2021-01-252021-01-2520201751-96591751-9667https://doi.org/10.1049/iet-ipr.2019.1051https://hdl.handle.net/11467/4459The concerns for a healthier diet are increasing day by day, especially in diabetics wherein the aim of healthier diet can only be achieved by keeping a track of daily food intake and glucose-level. As a consequence, there is an ever-increasing need for automatic tools able to help diabetics to manage their diet and also help physicians to better analyse the effects of various types of food on the glucose-level of diabetics. In this paper, we propose an intelligent food recognition and tracking system for diabetics, which is potentially an essential part of a mobile application that we propose to couple food intake with the blood glucose-level using glucose measuring sensors. For food recognition, we rely on several feature extraction and classification techniques individually and jointly using an early and three different late fusion techniques, namely (i) Particle Swarm Optimisation (PSO), (ii) Genetic Algorithms (GA) based fusion and (iii) simple averaging. Moreover, we also evaluate the performance of several handcrafted and deep features and compare the results against state-of-the-art. In addition, we collect a large-scale dataset containing images from several types of local Middle-Eastern food, which is intended to become a powerful support tool for future research in the domain.eninfo:eu-repo/semantics/closedAccessfeature extractiongenetic algorithmsdiseasesimage fusionsugarbloodparticle swarm optimisationimage classificationmedical image processingobject trackingautomatic toolsdiabeticsintelligent food recognitiontracking systemmobile applicationblood glucose levelglucose measuring sensorsfeature extractionclassification techniquesearly fusion techniquesgenetic algorithm-based fusionlocal middle-eastern foodautomatic food recognition systemmiddle-eastern cuisineshealthier dietdaily food intakeparticle swarm optimisationlarge-scale dataset collectionArticle141124692479Q3WOS:000571201500019Q22-s2.0-8509144741910.1049/iet-ipr.2019.1051