Fast CRP Segmentation via Machine Learning in Paper Microfluidics

Abstract

This paper presents a novel methodology for rapid C-reactive protein (CRP) segmentation in paper-based microfluidic devices leveraging machine learning techniques. By harnessing the computational power of machine learning algorithms, our approach achieves fast and accurate CRP segmentation, enhancing the efficiency of point-of-care diagnostics. Experimental validation demonstrates the effectiveness of our method in achieving precise CRP segmentation, highlighting its potential for improving healthcare accessibility and patient management. The integration of machine learning into paper Microfluidics opens new avenues for accelerated and sensitive CRP analysis, with promising implications for early disease detection and personalized medicine. Top of FormIn the medical diagnostics, the rapid and accurate segmentation of C-reactive protein (CRP) holds significant importance for early disease detection and monitoring. This paper introduces a novel approach for fast CRP segmentation utilizing machine learning within paper-based microfluidic devices. Leveraging the inherent advantages of paper microfluidics and the computational power of machine learning algorithms, our methodology enables swift and precise CRP segmentation, facilitating sensitive analysis in point-of-care settings. Experimental validations demonstrate the efficacy of our approach, showcasing improved segmentation speed and accuracy compared to traditional methods. The integration of machine learning into paper microfluidics represents a promising advancement in diagnostic capabilities, with implications for enhancing healthcare accessibility and patient outcomes.