An Attention‐Assisted Machine Learning System for Deep Microorganism Image Classification

Yujie Li, Ailing Gao, Guoxu Liu, Chunlei Chen, Yonghui Zhang, Sunkyoung Kang, M. Khosravi

Advances in microbiology and environmental health fundamentally depend on precise and timely microorganism identification based on advanced machine learning systems. We present a state‐of‐the‐art deep learning framework for high‐accuracy image‐based classification, leveraging a DenseNet201 backbone augmented with attention mechanisms to address noise, inter‐class similarity, and morphological diversity. Trained and fine‐tuned on 788 images spanning eight classes, the model attains an accuracy of 87.38%, a gain of ∼5% over nonadapted models. Its scalability, computational efficiency, and reduced reliance on chemical reagents position it as an environmentally sustainable and versatile solution with broad applicability across clinical, environmental, and industrial microbiology.