Classification of gram-positive and gram-negative bacteria using Few-shot learning algorithm
1 Department of Data Science and Business systems, Faculty of Engineering and Technology SRM Institute of Science and Technology, India.
2 Department of Data Science and Business systems, School of Computing, SRM Institute of Science and Technology, India.
Review Article
World Journal of Advanced Research and Reviews, 2024, 22(02), 516–523
Publication history:
Received on 31 March 2024; revised on 06 May 2024; accepted on 08 May 2024
Abstract:
Our research introduces a novel approach to classify bacteria as Gram-positive or Gram-negative using few-shot learning. We employ deep neural networks, specifically Prototypical Networks, to learn distinctive features from bacterial images, enabling accurate classification even with limited data. Experimental results on diverse datasets demonstrate the model's effectiveness and potential for real-world applications in microbiology and healthcare. We also address interpretability, ethics, and data privacy, making it a valuable tool for bacterial classification and diagnostics.
Keywords:
Gram Bacteria; Gram Positive Bacteria; Gram Negative Bacteria; Few-Shot Learning; Siamese Network
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