Mobile Food Journalling Application with Convolutional Neural Network and Transfer Learning: A Case for Diabetes Management in Malaysia

Jason Thomas Chew, Patrick Hang Hui Then, Yakub Sebastian, Valliappan Raman

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    Abstract

    Diabetes is an ever worsening problem in modern society, placing a heavy burden on healthcare systems. Due to the association between obesity and diabetes, food journaling mobile applications are an effective approach for managing and improving the outcome of diabetics. Due to the efficacy of nutritional tracking and management in managing diabetes, we implemented a deep learning-based Convolutional Neural Network food classification model to aid with food logging. The model is trained on a subset of the Food-101 and Malaysian Food 11 datasets, including web-scraped images, with a focus on food items found locally in Malaysia. In our experiments, we explore how fine-tuning of the image dataset improves the performance of the model.
    Original languageEnglish
    Pages (from-to)731-737
    Number of pages7
    JournalInternational Journal of Advanced Computer Science and Applications
    Volume13
    Issue number9
    DOIs
    Publication statusPublished - 2022

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