Streptococci Recognition in Microscope Images Using Taxonomy-based Visual Features

Abstract

This study explores the development of classifiers for microbial images, specifically focusing on streptococci captured via microscopy of live samples. Our approach uses AutoML-based techniques and automates the creation and analysis of feature spaces to produce optimal descriptors for classifying these microscopic images. This technique leverages interpretable taxonomic features based on the external geometric attributes of various microorganisms. We have released an annotated dataset we assembled to validate our solution, featuring microbial images from unfixed microscopic scenes. Additionally, we assessed the classification performance of our method against several classifiers, including those employing deep neural networks. Our approach outperformed all others tested, achieving the highest Precision (0.980), Recall (0.979), and F1-score (0.980).