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Abstract <p>This paper describes our research on creating classifiers for microbial images (micrococci microscopy images) obtained from pictures of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it possible to use interpretable taxonomic features based on the geometric features of the visual series of images of microorganisms of various species, which is important for the microbiology domain environment. To demonstrate the effectiveness of our method, we publish an annotated dataset we created consisting of microbial images of unfixed microscopic scenes. Using the presented data set, we compare the classification efficiency of our method and various types of classifiers, including those based on deep neural network models. The method we proposed demonstrated the best results among those studied (F1-score = 0.997).</p>

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Automated Feature Engineering-Based Approach for Micrococci Microscopic Image Classification and Taxonomic Characteristics Determination

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Aleksei Samarin,
  • Alexander Savelev,
  • Aleksei Toropov,
  • Artem Nazarenko,
  • Alexander Motyko,
  • Egor Kotenko,
  • Alexandra Dozorceva,
  • Alina Dzestelova,
  • Elena Mikhailova,
  • Valentin Malykh

摘要

Abstract

This paper describes our research on creating classifiers for microbial images (micrococci microscopy images) obtained from pictures of unfixed microscopic scenes. In our work, we propose an AutoML approach based on the automatic generation and analysis of the feature space for constructing the most optimal descriptors of microorganism images for subsequent classification. This makes it possible to use interpretable taxonomic features based on the geometric features of the visual series of images of microorganisms of various species, which is important for the microbiology domain environment. To demonstrate the effectiveness of our method, we publish an annotated dataset we created consisting of microbial images of unfixed microscopic scenes. Using the presented data set, we compare the classification efficiency of our method and various types of classifiers, including those based on deep neural network models. The method we proposed demonstrated the best results among those studied (F1-score = 0.997).