Machine learning algorithms in microbial classification: a comparative analysis

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 g...

Full description

Bibliographic Details
Main Authors: Yuandi Wu, S. Andrew Gadsden
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-10-01
Series:Frontiers in Artificial Intelligence
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2023.1200994/full
_version_ 1797656039776059392
author Yuandi Wu
S. Andrew Gadsden
author_facet Yuandi Wu
S. Andrew Gadsden
author_sort Yuandi Wu
collection DOAJ
description 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.
first_indexed 2024-03-11T17:23:30Z
format Article
id doaj.art-45baac2faeea4a1895961c5dd640c7a6
institution Directory Open Access Journal
issn 2624-8212
language English
last_indexed 2024-03-11T17:23:30Z
publishDate 2023-10-01
publisher Frontiers Media S.A.
record_format Article
series Frontiers in Artificial Intelligence
spelling doaj.art-45baac2faeea4a1895961c5dd640c7a62023-10-19T07:46:59ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122023-10-01610.3389/frai.2023.12009941200994Machine learning algorithms in microbial classification: a comparative analysisYuandi WuS. Andrew GadsdenThis 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.https://www.frontiersin.org/articles/10.3389/frai.2023.1200994/fullmachine learningdeep learningconvolutional neural networkstransfer learningbacterial classification
spellingShingle Yuandi Wu
S. Andrew Gadsden
Machine learning algorithms in microbial classification: a comparative analysis
Frontiers in Artificial Intelligence
machine learning
deep learning
convolutional neural networks
transfer learning
bacterial classification
title Machine learning algorithms in microbial classification: a comparative analysis
title_full Machine learning algorithms in microbial classification: a comparative analysis
title_fullStr Machine learning algorithms in microbial classification: a comparative analysis
title_full_unstemmed Machine learning algorithms in microbial classification: a comparative analysis
title_short Machine learning algorithms in microbial classification: a comparative analysis
title_sort machine learning algorithms in microbial classification a comparative analysis
topic machine learning
deep learning
convolutional neural networks
transfer learning
bacterial classification
url https://www.frontiersin.org/articles/10.3389/frai.2023.1200994/full
work_keys_str_mv AT yuandiwu machinelearningalgorithmsinmicrobialclassificationacomparativeanalysis
AT sandrewgadsden machinelearningalgorithmsinmicrobialclassificationacomparativeanalysis