Machine learning algorithms in microbial classification: a comparative analysis

Abstract

This research paper presents an overview of contemporary machine learning methodologies and their utilization in the domain of healthcare and the prevention of infectious diseases, specifically focusing on the classification and identification of bacterial species. As deep learning techniques have gained prominence in the healthcare sector, a diverse array of architectural models has emerged. Through a comprehensive review of pertinent literature, multiple studies employing machine learning algorithms in the context of microbial diagnosis and classification are examined. Each investigation entails a tabulated presentation of data, encompassing details about the training and validation datasets, specifications of the machine learning and deep learning techniques employed, as well as the evaluation metrics utilized to gauge algorithmic performance. Notably, Convolutional Neural Networks have been the predominant selection for image classification tasks by machine learning practitioners over the last decade. This preference stems from their ability to autonomously extract pertinent and distinguishing features with minimal human intervention. A range of CNN architectures have been developed and effectively applied in the realm of image classification. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. This method involves repurposing the knowledge gleaned from solving alternate image classification challenges to accurately classify microbial images. Consequently, the necessity for extensive and varied training data is significantly mitigated. This study undertakes a comparative assessment of various popular pre-trained CNN architectures for the classification of bacteria. The dataset employed is composed of approximately 660 images, representing 33 bacterial species. To enhance dataset diversity, data augmentation is implemented, followed by evaluation on multiple models including AlexNet, VGGNet, Inception networks, Residual Networks, and Densely Connected Convolutional Networks. The results indicate that the DenseNet-121 architecture yields the optimal performance, achieving a peak accuracy of 99.08%, precision of 99.06%, recall of 99.00%, and an F1-score of 98.99%. By demonstrating the proficiency of the DenseNet-121 model on a comparatively modest dataset, this study underscores the viability of transfer learning in the healthcare sector for precise and efficient microbial identification. These findings contribute to the ongoing endeavors aimed at harnessing machine learning techniques to enhance healthcare methodologies and bolster infectious disease prevention practices.

Editorial: Detection, characterization, and management of plant pathogens

“…Utilizing cutting-edge technologies like proteomics and genomics, new methods for extremely accurate identification and characterization of the infections can be devised through investigating their genetic and protein markers ( Zubair et al., 2022 ). Large data sets can also be analyzed by machine learning algorithms, which may provide fresh perspectives on the biology of bacteria that are pathogenic or beneficial as well as innovative approaches to management and diagnosis ( Wu and Gadsden, 2023 ). This section may deepen our knowledge of both beneficial and pathogenic bacteria and encourage the creation of better management strategies for diseases carried on by linked bacterial infections.…”

A machine learning-based strategy to elucidate the identification of antibiotic resistance in bacteria

“…Owing to the enormous amounts of data collected, microbiology has now emerged into a field with big data competencies (Falony et al, 2015;Kyrpides et al, 2016;Goodswen et al, 2021). Utilization of machine learning (ML) techniques for analysis of data has become a proven strategy in acquiring insights about microorganisms (Aida et al, 2022;Jiang et al, 2022;Munjal et al, 2022;Wu and Gadsden, 2023). Comprehensive studies on drug target prediction, drug resistance against antimicrobial drugs, prediction of disease outbreaks, and exploration of microbial-host interactions are now being carried out using ML techniques (Cazer et al, 2021;Kim and Ahn, 2021;Salim et al, 2021;Sudhakar et al, 2021;Kuang et al, 2022;Joshi et al, 2024).…”

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“…Owing to the enormous amounts of data collected, microbiology has now emerged into a field with big data competencies (Falony et al, 2015;Kyrpides et al, 2016;Goodswen et al, 2021). Utilization of machine learning (ML) techniques for analysis of data has become a proven strategy in acquiring insights about microorganisms (Aida et al, 2022;Jiang et al, 2022;Munjal et al, 2022;Wu and Gadsden, 2023). Comprehensive studies on drug target prediction, drug resistance against antimicrobial drugs, prediction of disease outbreaks, and exploration of microbial-host interactions are now being carried out using ML techniques (Cazer et al, 2021;Kim and Ahn, 2021;Salim et al, 2021;Sudhakar et al, 2021;Kuang et al, 2022;Joshi et al, 2024).…”