$$\beta $$-Hemolysis Detection on Cultured Blood Agar Plates by Convolutional Neural Networks

Abstract

The recent introduction of Full Laboratory Automation (FLA) systems in Clinical Microbiology opens to the availability of huge streams of high definition diagnostic images representing bacteria colonies on culturing plates. In this context, the presence of \(\beta \)-hemolysis is a key diagnostic sign to assess the presence and virulence of pathogens like streptococci and to characterize major respiratory tract infections. Since it can manifest in a high variety of shapes, dimensions and intensities, obtaining a reliable automated detection of \(\beta \)-hemolysis is a challenging task, never been tackled so far in its real complexity. To this aim, here we follow a deep learning approach operating on a database of 1500 fully annotated dual-light (top-lit and back-lit) blood agar plate images collected from FLA systems operating in ordinary clinical conditions. Patch-based training and test sets are obtained with the help of an ad-hoc, total recall, region proposal technique. A DenseNet Convolutional Neural Network architecture, dimensioned and trained to classify patch candidates, achieves a 98.9% precision with a recall of 98.9%, leading to an overall 90% precision and 99% recall on a plate basis, where false negative occurrence needs to be minimized. Being the first approach able to detect \(\beta \)-hemolysis on a whole plate basis, the obtained results open new opportunities for supporting diagnostic decisions, with an expected high impact on the efficiency and accuracy of the laboratory workflow.