<![CDATA[While conventional culture-based, biochemical, and molecular identification methods remain fundamental in microbiology, computational image analysis can serve as an exploratory, supplementary tool for studying bacterial morphology. This proof-of-concept study evaluates a hybrid VGG16-LSTM model for microscopic bacterial image classification. This study utilized a small subset of the DIBaS dataset consisting of six bacterial classes: Acinetobacter baumannii, Escherichia coli, Lactobacillus plantarum, Micrococcus spp., Propionibacterium acnes, and Pseudomonas aeruginosa. The total dataset size is highly constrained at 124 images, with a correspondingly small number of images per class ranging from 20 to 23. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. All microscopic images were resized to 224 × 224 pixels, normalized, and dynamically augmented during training to improve data variability under these limited-sample conditions. A pre-trained VGG16 network was employed to extract spatial image features, and the final convolutional feature map was reshaped into a spatial sequence and processed using an LSTM layer for further feature learning. Three optimization algorithms, namely Adam, RMSprop, and stochastic gradient descent, were compared. Among them, the RMSprop-optimized model exhibited the highest metrics after 50 epochs, achieving 92.86% accuracy, 95.24% precision, 92.86% recall, and a 92.38% F1-score; however, these performance indicators must be interpreted with caution as they are based on a severely limited test set (approximately 12 images). Some misclassifications occurred between P. aeruginosa and E. coli, which may be attributed to their shared gram-negative rod-shaped morphology. This finding highlights the inherent biological challenge of distinguishing visually similar bacterial species from microscopic image analysis alone, underscoring that such models remain strictly experimental and are not suited for clinical-grade diagnosis or field-ready environmental screening.]]>
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