A Brief Review for Content-Based Microorganism Image Analysis Using Classical and Deep Neural Networks

Li, Chen; Xu, Ning; Jiang, Tao; Qi, Shouliang; Han, Fangfang; Qian, Wei; Zhao, Xin

doi:10.1007/978-3-319-91211-0_1

Cited by 8 publications

(13 citation statements)

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“…Besides, this paper also discusses potential techniques for the image analysis of microorganism by ANNs. As far as we know, there are some review papers that summarize researches related to the MIA task, for example, papers [70,78,80,81]. In the following part, we go through these review papers.…”

Section: Motivation Of This Reviewmentioning

confidence: 99%

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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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Section: Motivation Of This Reviewmentioning

confidence: 99%

“…In our previous work [81], we propose a brief review for content-based microorganism image analysis using classical and deep neural networks. This review briefly summarizes 55 related papers from 1992 to 2017.…”

Section: Motivation Of This Reviewmentioning

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

Self Cite

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“…Deep learning tools such as Convolutional Neural Networks (CNN) are well suited for automating classification and quantification of bacterial phenotypes present in microscope images; they have been applied in other microbiology tasks such as classification of coccoliths formed by various coccolithophores, stalked protozoa identification, and bacterial plankton classification [12][13][14][15]. CNN transform an image volume to a linear output volume (holding the class scores) using a stack of interconnected convolutional, pooling, and fully-connected layers.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of bacterial cell sub-populations with convolutional neural networks

2020

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“…It is noted that there exist some The associate editor coordinating the review of this manuscript and approving it for publication was Habib Ullah. review works on image segmentation techniques, for example, [2]- [4] and [5]. However, these reviews are based on segmentation of non-microorganism images except only in our previous review in [5], which gives overview on microorganism image analysis (partially, including segmentation).…”

Section: Introductionmentioning

confidence: 99%

“…review works on image segmentation techniques, for example, [2]- [4] and [5]. However, these reviews are based on segmentation of non-microorganism images except only in our previous review in [5], which gives overview on microorganism image analysis (partially, including segmentation). Furthermore, no concrete conclusions are pinpointed on the best segmentation methods for particular microorganism segmentation challenges or most frequently used ones.…”

Section: Introductionmentioning

confidence: 99%

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

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A Brief Review for Content-Based Microorganism Image Analysis Using Classical and Deep Neural Networks

Cited by 8 publications

References 40 publications

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

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

Automated classification of bacterial cell sub-populations with convolutional neural networks

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

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