Search

Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium. A total of 200 dig¬ital microscopic cell images comprising 40 of each of the genera mentioned above were used in this study. Results: The developed technique could identify and distinguish microscopic images of Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium with the highest accuracy of 92.20% for Staphylococcus and the lowest of 77.40% for Salmonella. Among the five epochs, the accuracy rate of bacterial genera identification of Staphylococcus was graded 1, and Streptococcus, Escherichia, Corynebacterium, and Salmonella as 2, 3, 4, and 5, respectively. Conclusion: The experimental results suggest using the DL method to predict bacterial genera included in this study. However, further improvement with more bacterial genera, especially of similar morphology, is necessary to make the technique widely used for bacterial genera identification. [J Adv Vet Anim Res 2022; 9(4.000): 573-582]

Objectives: This study aimed to develop a computerized deep learning (DL) technique to identify bacterial genera more precisely in minimum time than the usual, traditional, and commonly used techniques like cultural, staining, and morphological characteristics. Materials and Methods: A convolutional neural network as a part of machine learning (ML) for bacterial genera identification methods was developed using python programming language and the Keras API with TensorFlow ML or DL framework to discriminate bacterial genera, e.g., Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium. A total of 200 digital microscopic cell images comprising 40 of each of the genera mentioned above were used in this study. Results: The developed technique could identify and distinguish microscopic images of Streptococcus, Staphylococcus, Escherichia, Salmonella, and Corynebacterium with the highest accuracy of 92.20% for Staphylococcus and the lowest of 77.40% for Salmonella. Among the five epochs, the accuracy rate of bacterial genera identification of Staphylococcus was graded 1, and Streptococcus, Escherichia, Corynebacterium, and Salmonella as 2, 3, 4, and 5, respectively. Conclusion: The experimental results suggest using the DL method to predict bacterial genera included in this study. However, further improvement with more bacterial genera, especially of similar morphology, is necessary to make the technique widely used for bacterial genera identification. [ABSTRACT FROM AUTHOR]