Rapid detection and classification of bacterial colonies using a thin film transistor (TFT) image sensor and deep learning (Conference Presentation)

Abstract

We present a high-throughput and automated system for the early detection and classification of bacterial colony-forming units (CFUs) using a thin-film transistor (TFT) image sensor. A lens-free imager was built using the TFT sensor with a ~7 cm2 field-of-view to collect the time-lapse images of bacterial colonies. Two trained neural networks were used to detect and classify the bacterial colonies based on their spatio-temporal features. Our system achieved an average CFU detection rate of 97.3% at 9 hours of incubation and an average CFU recovery rate of 91.6% at ~12 hours, saving ~12 hours compared to the EPA-approved method.