A Deep (Learning) Dive Into Bacterial Classification

Most bacteria classifiers created by neural networks and/or image processing methods are unable to generalize when used with different data bases of images acquired with the same type of acquisition systems, even if the sample preparation is similar. In this work, we introduce an ensemble of deep neural networks designed for the classification of bacteria in a broad context. We use a dataset comprising Actinomyces, Escherichia, Staphylococcus, Lactobacillus, and Micrococcus bacteria with Gram staining, which was acquired through brightfield microscopy from various sources. To normalize diversity of image characteristics, we applied domain generalization and adaptation techniques. Subsequently, we used phenotypic characteristics, such as the color reaction to Gram staining and morphology, to classify the bacteria.