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Amid the pandemic, screen interactions have surged by 60-80%, accentuating digital eye strain, attention
deficits, and posture issues. This study addresses a key challenge in image analysis post-acquisition,
especially in scanning electron microscopy (SEM) critical for health and material sciences applications.
For instance, surface morphologies in cancer research and fiber quantification in biomaterials demand
precise analysis, often hindered by time constraints and human error. This project proposes a machine
learning workflow to accurately quantify SEM images, leveraging previously quantified SEM images to
create a high-quality training dataset. Concurrently, a community engagement pipeline is devised to
involve citizen scientists in SEM analysis, beginning with a pilot
study and extending to interactive sessions at the Virginia Tech Science Festival. This dual approach
aims to bolster SEM analysis accuracy and foster a reciprocal educational ecosystem, setting a strong
foundation for future machine learning applications and community-driven scientific explorations.
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