Speed meets accuracy: Advanced deep learning for efficient Orientia tsutsugamushi bacteria assessment in RNAi screening

Abstract

This study investigates the use of advanced computer vision techniques for assessing the severity of Orientia tsutsugamushi bacterial infectivity. It uses fluorescent scrub typhus images obtained from molecular screening, and addresses challenges posed by a complex and extensive image dataset, with limited computational resources. Our methodology integrates three key strategies within a deep learning framework: transitioning from instance segmentation (IS) models to an object detection model; reducing the model's backbone size; and employing lower-precision floating-point calculations. These approaches were systematically evaluated to strike an optimal balance between model accuracy and inference speed, crucial for effective bacterial infectivity assessment. A significant outcome is that the implementation of the Faster R-CNN architecture, with a shallow backbone and reduced precision, notably improves accuracy and reduces inference time in cell counting and infectivity assessment. This innovative approach successfully addresses the limitations of image processing techniques and IS models, effectively bridging the gap between sophisticated computational methods and modern molecular biology applications. The findings underscore the potential of this integrated approach to enhance the accuracy and efficiency of bacterial infectivity evaluations in molecular research.