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Colorimetric Sensor Reading and Illumination Correction via Multi-Task Deep-Learning.

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    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
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    This study introduces deep learning methods to accurately interpret colorimetric sensor data, even under challenging lighting. This enhances the reliability of these sensors for widespread use in diagnostics and monitoring.

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    Area of Science:

    • Nanotechnology
    • Computational Science
    • Sensor Technology

    Background:

    • Colorimetric sensors offer accessible nanotechnology for measuring substance properties via color changes.
    • Current interpretation methods (visual or controlled camera setups) limit real-world applications due to altered light conditions and lower resolutions.
    • There is a need for robust methods to interpret colorimetric sensor data accurately in non-ideal environments.

    Purpose of the Study:

    • To develop and evaluate image processing and deep learning (DL) methods for accurate colorimetric sensor reading under non-uniform illumination.
    • To compare the performance of independent versus joint (multi-task) DL models for denoising and regression tasks.
    • To improve the accessibility and accuracy of colorimetric sensors for end-user applications.

    Main Methods:

    • Collected video recordings of colorimetric sensors measuring temperature to create a reference dataset.
    • Augmented sensor images with non-uniform color alterations to simulate real-world conditions.
    • Implemented and evaluated various DL architectures, including a multi-task model that disentangles luminance, chrominance, and noise.

    Main Results:

    • The best-performing DL architecture achieved a mean squared error (MSE) of 0.811±0.074[°C] and r²=0.930±0.007.
    • This multi-task model demonstrated a significant improvement of 1.26[°C] MSE compared to independent denoising and regression tasks.
    • The model accurately predicted sensor readings despite strong color perturbations and non-uniform illumination.

    Conclusions:

    • The proposed DL methodology enhances the accuracy of colorimetric sensor readings in altered illumination conditions.
    • This approach increases the potential for large-scale accessibility of colorimetric sensors in point-of-care diagnostics and continuous health monitoring.
    • The developed methods overcome limitations of visual interpretation and controlled camera setups, broadening sensor applicability.