Gluten identification from food images using advanced deep learning and transfer learning methods

Abstract

Food image recognition has become an essential application in computer vision, with significant implications for dietary management, particularly for individuals with specific dietary restrictions. This paper shows a novel approach for gluten image classification, designed to assist individuals with celiac disease in identifying gluten-containing foods. Our proposed model leverages a Convolutional Neural Network (CNN) architecture, specifically utilizing a EfficientNet pretrained model, to accurately identify and classify food images. In the proposed model We utilized a curated dataset from the Food101 dataset, selecting 20,000 images focused on common food recipes. We used the EfficientNet pretrained model, achieving a training accuracy of 99.02% and a validation accuracy of 98.38%. The model was further evaluated on 2000 test images, obtaining an accuracy of 99%. The data was meticulously labelled to ensure high-quality training as well as testing processes. Our results demonstrate the model’s effectiveness in gluten classification, highlighting its potential utility for celiac patients. This work contributes to the growing field of food image recognition and offers a valuable tool for dietary management in celiac patients.