Application of Computer Vision Transformer Algorithm in Probiotic Micromorphological Feature Extraction and Strain Typing

Probiotic research faces challenges in accurately extracting microscopic morphological features and differentiating highly similar strains using inefficient traditional methods like manual microscopy and genomic techniques. To address this, this study introduces a novel computer vision approach leveraging the Transformer algorithm. The methodology first automates microscopic image processing using contrast enhancement and brightness correction. Subsequently, it integrates a Transformer-based deep learning model with multi-scale feature extraction and a self-attention mechanism to analyze processed images. Genomic data (FASTQ format) is concurrently processed through quality control, assembly, and annotation, then fused with visual features to optimize the model. Finally, the combined data trains the Transformer architecture using Adam optimization and regularization techniques. Experimental results demonstrate significant improvements: 93-96% feature extraction accuracy (surpassing SIFT/HOG by >15%), 97-99% strain typing accuracy, and 97.995% discrimination for highly similar strains. The model also processes images <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2-3 \times$</tex> faster than traditional methods, validating its efficacy for precise probiotic analysis and functional prediction.