Rapid Pathogen Identification Through Multimodal Sensor Fusion and Deep Learning

Abstract

The rapid and accurate identification ofpathogenicmicroorganismsrepresents one of the most pressing challenges in clinical microbiology, food safety, and environmental monitoring.  Conventional culture-based methods, while reliable, typically require 24 to 72 hours to yield actionable results, a limitation that has profound implications for patient outcomes in time-sensitive infectious disease scenarios. This work proposes an integrated framework that combines multimodal sensor fusion (MSF) with deep learning to achieve sub-hour pathogen identification across clinically relevant species. The system simultaneously acquires spectroscopic signatures from surface-enhanced Raman scattering, volatile organic compound (VOC) profiles from an electronic nose array, and impedance spectroscopy features, fusing these complementary modalities through a hierarchical convolutional attention network. The architecture draws upon a parallel multipathway feature extraction design in which each sensing channel  undergoes dedicated representation learning before contributing to  a joint classification decision. Experimental validation on a datasetspanning twelve bacterial species demonstrates an aggregateclassification accuracy of 97.8%, with a mean time-to-result of 38 minutes from sample introduction. Classification agreementbetween the proposed system and trained microbiologists approaches the level of inter-expert agreement, and per-species performance comparison confirms that combined feature representations systematically outperform single-channel baselines. The proposed approach substantially outperforms unimodal baselines and establishes a viable pathway toward deployable, rapid diagnostic platforms for point-of-care settings.