Improvement in Microbial Classification Quality Using Synthetic Microscopic Images Generated by Large Visual-Language Models

Pattern Recognition and Image Analysis. 2026. Vol. 36. No. 2. P. 302–312.

Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Kotenko E., Dozortseva A., Nazarenko A., Mamaeva A., Motyko A., Elena Mikhailova, Valentin Malykh

The lack of annotated microscopic datasets remains a major obstacle to training robust deep learning models for microbial classification. In this paper, a novel data augmentation pipeline that uses visual–linguistic large-scale models to generate synthetic microscopic images of six different bacterial and nonbacterial classes has been proposed. Synthetic samples have gradually been added to the training set in controlled proportions in order to evaluate systematically their impact on the quality of the model. Quantitative experiments have shown that including up to 11% synthetic data leads to a significant increase in classification accuracy: the best configuration achieves a Precision of 0.91, a Recall of 0.89, and an F1-score of 0.90. However, when this saturation threshold is exceeded, a decrease in quality is observed, indicating possible introduction of distribution noise or overfitting due to excessive synthetic augmentation. The results have demonstrated the potential of generating synthetic data based on visual–linguistic large-scale models as a scalable approach to addressing class imbalance and data shortage issues in microbiological image analysis problems.

Keywords: image classification, biomedical image processing, microorganism recognition