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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Related Experiment Video

Updated: Jul 8, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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Automatic Chinese Food recognition based on a stacking fusion model.

Bokun Fan, Weiqi Li, Liang Dong

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study developed a deep learning model for precise dietary recording using smartphone images of Chinese dishes. The AI model achieved high accuracy, aiding individuals managing conditions like diabetes and obesity.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Food Science

    Background:

    • Deep learning models are increasingly commercialized, enabling smartphone-based dietary record keeping.
    • Accurate food recognition is crucial for dietary management, especially for health conditions like obesity and diabetes.

    Purpose of the Study:

    • To develop a big-data-driven deep learning model for recognizing Chinese dishes from images.
    • To establish the largest dataset of Chinese dishes (CNFOOD-241) for training and evaluation.
    • To evaluate and compare the performance of popular deep learning models and introduce a novel multi-model fusion technique.

    Main Methods:

    • Created CNFOOD-241, a dataset with over 190,000 images across 241 Chinese dish categories.
    • Trained and evaluated three popular deep learning models, including ResNeXt101_32x32d.
    • Implemented a novel multi-model fusion method using a meta-learner to integrate base models for improved robustness.

    Main Results:

    • ResNeXt101_32x32d achieved 82.05% top-1 and 97.13% top-5 accuracy on the CNFOOD-241 dataset.
    • The multi-model fusion approach reached 82.88% top-1 accuracy, demonstrating enhanced performance.
    • The study confirmed the feasibility of AI in identifying Chinese dishes.

    Conclusions:

    • AI-powered image recognition of Chinese dishes is feasible and accurate.
    • This technology can significantly assist individuals requiring dietary control, such as those with diabetes or obesity.
    • The developed CNFOOD-241 dataset and fusion model advance the field of food recognition.