Healthy vs. Unhealthy Food Images: Image Classification of Twitter Images

Tejaswini Oduru, Alexis Jordan and Albert Park
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Tejaswini Oduru: Department of Software and Information, Systems College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC 28223, USA
Alexis Jordan: Department of Software and Information, Systems College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC 28223, USA
Albert Park: Department of Software and Information, Systems College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC 28223, USA

IJERPH, 2022, vol. 19, issue 2, 1-12

Abstract: Obesity is a modern public health problem. Social media images can capture eating behavior and the potential implications to health, but research for identifying the healthiness level of the food image is relatively under-explored. This study presents a deep learning architecture that transfers features from a 152 residual layer network (ResNet) for predicting the level of healthiness of food images that were built using images from the Google images search engine gathered in 2020. Features learned from the ResNet 152 were transferred to a second network to train on the dataset. The trained SoftMax layer was stacked on top of the layers transferred from ResNet 152 to build our deep learning model. We then evaluate the performance of the model using Twitter images in order to better understand the generalizability of the methods. The results show that the model is able to predict the images into their respective classes, including Definitively Healthy, Healthy, Unhealthy and Definitively Unhealthy at an F1-score of 78.8%. This finding shows promising results for classifying social media images by healthiness, which could contribute to maintaining a balanced diet at the individual level and also understanding general food consumption trends of the public.

Keywords: obesity; image classification; social media; twitter; food image (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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