Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks

Kang, Rui; Park, Bosoon; Eady, Matthew; Ouyang, Qin; Chen, Kunjie

doi:10.1016/j.snb.2020.127789

Cited by 46 publications

(23 citation statements)

References 28 publications

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“…However, both the quality and quantity of data are insufficient for creating effective ML model. Some researchers ([ 45 , 62 , 64 , 65 ]) merge one ML technique with other ML techniques, like SVM with radial basis function, CNN with random projection and DL with LSTM, ResNet and 1D-CNN to produce hybrid model for bacterial image classification. However the desired efficiency of the model is yet to be attained due to poor data quality.…”

Section: Discussionmentioning

confidence: 99%

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Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Kotwal

Rani

Arif

et al. 2021

Arch Computat Methods Eng

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Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998–2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field.

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Section: Discussionmentioning

confidence: 99%

“…Kang et al [ 64 ]developed multiple advanced DL frameworks i.e. long-short term memory (LSTM) network, deep residual network (ResNet), and one dimensional CNN (1D-CNN) for the classification of food-borne bacteria using Hyperspectral Microscopic imaging (HMI) technology.…”

Section: In Bacterial Image Analysismentioning

confidence: 99%

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Kotwal

Rani

Arif

et al. 2021

Arch Computat Methods Eng

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“…Kang et al [19] designed a set of advanced deep-learning frameworks, including the long short-term memory (LSTM) network, the deep residual network (ResNet), and the one-dimensional convolutional neural network (1D-CNN), for the classification of foodborne bacteria using hyperspectral microscopic imaging (HMI) technology. Five popular foodborne bacterial cultures (Campylobacter jejuni, generic E. coli, Listeria innocua, Staphylococcus aureus, and Salmonella typhimurium) were collected by the U.S. Department of Agriculture's Poultry Microbiological Safety and Processing Research Unit (PM-SPRU) in Athens, Georgia.…”

Section: Related Workmentioning

confidence: 99%

Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Mai

Ishibashi

2021

Electronics

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Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable convolutional neural networks. Our five-layer architecture has significant advantages due to the compact model, low computational cost, and reliable recognition accuracy. The experimental results proved that the proposed design reached the highest accuracy of 96.28% with a total of 6600 images and can be executed on limited-resource devices of 3.23 million parameters and 40.02 million multiply–accumulate operations (MACs). The number of parameters in this architecture is seven times less than the smallest model listed in the literature.

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“…The presence of pathogenic microorganisms in food is a significant threat to consumers and the food industry. In [63], a high-throughput hyperspectral microscope imaging technology with a hybrid deep learning framework defined as "Fusion-Net" is proposed for rapid classification of foodborne bacteria at the single-cell level. The dataset used here contains five classes.…”

Section: Datasetmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks

Cited by 46 publications

References 28 publications

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

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