An automated lateral flow assay identification framework: Exploring the challenges of a wearable lateral flow assay in mobile application
نویسندگان
چکیده
Wearable lateral flow assay (LFA) devices in the point-of-care (POC) diagnosis are an emerging technology due to their ease of use, affordability, and robustness. Recent advances wearable allow rapid self-diagnosis using colorimetry. Visual readouts LFA strips often lead inaccurate readout different lighting conditions. Smartphone-based increasingly popular among POC platforms. Myriad challenges exist smartphone-based colorimetric analysis. Image processing on faces such as environment (indoor or outdoor), reflection (camera flash, shadow, etc.), conditions, illumination intensity (low high), blurred, a bent sensor skin. In this research, automated framework that tackles these through series image steps is proposed. A preliminary study focuses tackling some preprocessing object detection explored. Our accurately identifies body under various addition, challenging biomarker lines identified LFA. strategy handle reflection, high low illuminations camera flash illustrated improves sensitivity proposed framework. As opposed previous frameworks process white background, our captured real-time images used them for This research proposes prospective mobile application • Colorimetric immunoassay myriad challenges. Environmental identified. Propose novel detection. The biomarkers. An anti-reflection address recognition enhancement.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2022
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2022.118471