Fine-grained food image classification and recipe extraction using a customized deep neural network and NLP.
Comput Biol Med
; 175: 108528, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38718665
ABSTRACT
Global eating habits cause health issues leading people to mindful eating. This has directed attention to applying deep learning to food-related data. The proposed work develops a new framework integrating neural network and natural language processing for classification of food images and automated recipe extraction. It address the challenges of intra-class variability and inter-class similarity in food images that have received shallow attention in the literature. Firstly, a customized lightweight deep convolution neural network model, MResNet-50 for classifying food images is proposed. Secondly, automated ingredient processing and recipe extraction is done using natural language processing algorithms Word2Vec and Transformers in conjunction. Thirdly, a representational semi-structured domain ontology is built to store the relationship between cuisine, food item, and ingredients. The accuracy of the proposed framework on the Food-101 and UECFOOD256 datasets is increased by 2.4% and 7.5%, respectively, outperforming existing models in literature such as DeepFood, CNN-Food, Wiser, and other pre-trained neural networks.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Procesamiento de Lenguaje Natural
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Redes Neurales de la Computación
Límite:
Humans
Idioma:
En
Revista:
Comput Biol Med
/
Comput. biol. med
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Computers in biology and medicine
Año:
2024
Tipo del documento:
Article