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Published on: September 25, 2021
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Deep learning-based bacterial genus identification
Md Shafiur Rahman Khan1, Ishrat Khan2, Md Abdus Sattar Bag3
1Department of Computer Science and Mathematics, Faculty of Agricultural Engineering & Technology, Bangladesh Agricultural University, Mymensingh, Bangladesh.
Journal of Advanced Veterinary and Animal Research
|January 30, 2023
Summary
A new deep learning (DL) method accurately identifies bacterial genera from microscopic images. This AI approach offers a faster alternative to traditional methods for bacterial identification.
Area of Science:
- Microbiology
- Computer Science
- Artificial Intelligence
Background:
- Traditional bacterial identification methods, such as culturing and staining, are time-consuming and labor-intensive.
- Accurate and rapid identification of bacterial genera is crucial for timely diagnosis and treatment in clinical settings.
Purpose of the Study:
- To develop a computerized deep learning (DL) technique for precise and rapid identification of bacterial genera.
- To compare the efficiency of the DL method against conventional identification techniques.
Main Methods:
- A convolutional neural network (CNN) model was developed using Python, Keras, and TensorFlow.
- The model was trained and validated on 200 digital microscopic images of five bacterial genera: *Streptococcus*, *Staphylococcus*, *Escherichia*, *Salmonella*, and *Corynebacterium*.
Main Results:
- The DL technique achieved high accuracy in identifying bacterial genera, with *Staphylococcus* identified at 92.20% and *Salmonella* at 77.40%.
- The model demonstrated varying accuracy across genera, with *Staphylococcus* ranked highest and *Salmonella* lowest in identification accuracy within five epochs.
Conclusions:
- The developed DL method shows promise for bacterial genera prediction, offering a faster alternative to traditional methods.
- Further research is needed to enhance the technique's accuracy and expand its application to a wider range of bacterial genera, particularly those with similar morphologies.
Keywords:
Convolutional neural networkautomated bacteria identificationdeep learningdigital image bacterial generaMore Related Videos
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