Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable convolutional neural networks. Our five-layer architecture has significant advantages due to the compact model, low computational cost, and reliable recognition accuracy. The experimental results proved that the proposed design reached the highest accuracy of 96.28% with a total of 6600 images and can be executed on limited-resource devices of 3.23 million parameters and 40.02 million multiply–accumulate operations (MACs). The number of parameters in this architecture is seven times less than the smallest model listed in the literature.

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The significant bottlenecks in determining bacterial species are much more time-consuming and the biology specialist's long-term experience requirements. Specifically, it takes more than half a day to cultivate a bacterium, and then a skilled microbiologist and a costly specialized machine are utilized to analyze the genes and classify the bacterium according to its nucleotide sequence. To overcome these issues as well as get higher recognition accuracy, we proposed applying convolutional neural networks (CNNs) architectures to automatically classify bacterial species based on some key characteristics of bacterial colonies. Our experiment confirmed that the classification of three bacterial colonies could be performed with the highest accuracy (97.19%) using a training set of 5000 augmented images derived from the 40 original photos taken in the Hanoi Medical University laboratory in Vietnam.