Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges...

Area of Science:

• Microbiology
• Computer Science
• Bioinformatics

Background:

• Bacteria play crucial roles in various sectors, with some beneficial and many pathogenic species causing severe illnesses.
• Traditional bacterial detection methods (e.g., Gram staining, biochemical tests) are being augmented by advanced computational approaches.
• The increasing volume of data and advancements in computer science necessitate efficient automated bacterial classification.

Purpose of the Study:

• To review and critically analyze machine learning (ML) methodologies applied to bacterial classification.
• To cover the period from 1998 to 2020, synthesizing research from reputable publishers.
• To identify limitations, future scope, opportunities, and challenges in ML-based bacterial classification.

Main Methods:

• Literature review of research papers and book chapters on ML in bacterial classification.
• Analysis of various ML algorithms and their application in identifying and classifying bacteria.
• Critical evaluation of the performance, limitations, and scope of different ML techniques.

Main Results:

• Machine learning methods demonstrate high performance in automatic bacterial detection and classification.
• A wide array of ML approaches have been successfully applied to diverse bacterial classification tasks.
• The review consolidates findings on the efficacy and challenges of ML in microbiology.

Conclusions:

• Machine learning and Artificial Intelligence (AI) are transformative tools for microbiologists, enabling solutions to complex bacterial identification problems.
• Further research into ML methodologies can enhance accuracy, efficiency, and scope in bacterial classification.
• Addressing implementation challenges is key to fully leveraging AI in applied microbiology.