Kernel Representation-based End-to-End Network-enabled Decoding Strategy for Precise and Medical Diagnosis

Abstract

Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets. The model uses a shape-aware radial basis function to learn the kernel representation of objects, improving the target counting accuracy, and exhibits excellent performance in identifying adherent polystyrene microspheres, with a detection accuracy of 98.39%. Considering these factors, we developed a biotin–streptavidin-based biosensing method using artificial intelligence transcoding (bs-SMART) to detect procalcitonin in serum samples. Given its excellent accuracy and sensitivity (limit of detection = 8.5 pg/mL), the technique provides a reliable platform for the accurate diagnosis of diseases. Furthermore, this study validated the ability of CellNet to recognize irregular and adherent cells. Overall, CellNet not only contributes to advancing computer vision and image processing technology but also presents potential benefits for medical diagnostics, food safety testing, and environmental monitoring.