Identification of foods in the breakfast and determination of nutritional facts using deep convolution neural networks
DOI:
https://doi.org/10.5327/fst.10323Palavras-chave:
deep learning, food recognition, machine learning, nutritional value predictionResumo
Food recognition plays a crucial role in various fields, including healthcare, nutrition, and the food industry. It involves identifying different types of foods or dishes from images, videos, or other data sources. In healthcare, food recognition aids individuals in monitoring their daily food intake and managing their diet. It also assists dietitians and nutritionists in creating personalized meal plans based on patients' nutritional requirements and preferences. This article focuses on the development of software that can recognize food products and predict their nutritional facts. The software extracts essential nutritional facts such as fat, carbohydrates, protein, and energy from the food products and compiles them into a comprehensive list. For each of the 20 food products, 36 food images were obtained, resulting in a total of 720 food images. To validate the accuracy of the trained models, six different images of each food product were set aside for external validation purposes. The rest of the images were then trained using deep learning algorithms, namely, GoogleNet, ResNet-50, and Inception-v3, in the MATLAB software. The training and validation processes yielded over 98% correct predictions for each of the deep learning algorithms. Although there were no significant differences in accuracy among the algorithms, GoogleNet stood out when considering both prediction accuracy and prediction time. The validated deep learning algorithms were employed in developing the software for food recognition and nutritional value determination. The results indicate that the developed software can reliably identify foods and provide their corresponding nutritional facts. This software holds significant potential for application in the nutrition and dietetic field and can be particularly useful in healthcare settings for monitoring the dietary intake of patients with chronic diseases such as diabetes, heart disease, or obesity. The system can track the types and quantities of foods consumed, offering personalized feedback to patients and healthcare providers.
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