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Diseases caused by bacterial contamination are common causes of human illness. Different bacterial strains are responsible for different types of diseases. There are more than 4,900 different strains so far have been discovered. That is why it is impractical to start the treatment of diseases caused by bacterial attacks without diagnosing the particular strain that caused the diseases. The traditional method of bacterial strain classification from the specimens is still widely used in microbiological practice for clinical application. However, it s a time-consuming process and requires well-trained, experienced microbiologists. This paper proposes a computer-aided artificial intelligent-based automatic bacterial strain classification method that is faster than traditional methods and a potentially better alternative. We designed, optimized, and experimented with a Convolutional Neural Network (CNN) to automatically classify …