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

Kotwal, Shallu; Rani, Pooja; Arif, Tasleem; Manhas, Jatinder; Sharma, Sparsh

doi:10.1007/s11831-021-09660-0

Cited by 26 publications

(16 citation statements)

References 60 publications

(61 reference statements)

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“…It is not surprising then that identification of individuals or species from image, video, and sound data is the most common use of deep learning in the field (Figure 3). These efforts already span many taxa, from bacteria (Kotwal et al, 2021), through protozoans (Hsiang et al, 2019), plants (Carranza‐Rojas et al, 2017; Schuettpelz et al, 2017; Unger et al, 2016; Younis et al, 2018) to insects (Boer & Vos, 2018; Hansen et al, 2020; Marques et al, 2018; Valan et al, 2019) and vertebrates (Norouzzadeh et al, 2018; Villon et al, 2018), both extant and fossil (de Lima et al, 2020; Liu & Song, 2020; Miele et al, 2020) and at scales ranging from local to global.…”

Section: Applications In Ecology and Evolutionmentioning

confidence: 99%

Deep learning as a tool for ecology and evolution

et al. 2022

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Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns. In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline‐wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications. We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitoring and discovery and inference from genetic data. Increased use of unsupervised learning for discovery and visualization of clusters and gaps, simplification of multi‐step analysis pipelines, and integration of machine learning into graduate and postgraduate training are all likely in the near future.

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Section: Applications In Ecology and Evolutionmentioning

confidence: 99%

Deep learning as a tool for ecology and evolution

et al. 2022

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“…They can appear as one of these three types: uni-cellular (having one cell), multi-cellular (having many cells), and ac-cellular (no cells) [1]. Bacteria are single-cell microorganisms and specifically belong to a large domain called prokaryote which lacks a nucleus and membrane-bound organelles [2], [3]. They are ubiquitous, both inside and outside the human body, and vary greatly in appearance, shape, and size.…”

Section: Introductionmentioning

confidence: 99%

“…DIBaS dataset [4] is comprised of microscopic images and includes 20 images for each of the 33 distinct species of bacteria. Even though several studies have classified microorganisms, a survey conducted by Kotwal et al (2012) [3] pointed out that the availability of open datasets for microscopic pictures of bacteria is very limited. The majority of previous studies were conducted on private datasets.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Bacterial Classification Using Big Transfer (BiT)

Visitsattaponge,

Bunkum,

Pintavirooj

et al. 2024

IEEE Access

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Identification and classification of bacterial genera and species are very important for medical prevention, diagnosis, and treatment. However, due to microbial diversity and high variability in appearance, the manual classification of bacteria is a challenging and time-consuming task. This paper aims to facilitate such a troublesome task using deep learning techniques. Through the utilization of a deep learning model, specifically a Big Transfer (BiT) combined with Graph Laplacian-based data cleaning and weight initialization based-rectified linear unit (WIB-Relu) activation, we have developed an accurate bacteria classification model. We have tested our proposed method on a public dataset of microscopic bacteria images, called the Digital Images of Bacteria Species (DIBaS), and achieved promising results with an accuracy of 99.11%, precision of 99.31%, recall of 99.09%, and F1 score of 99.06%, respectively. Moreover, the proposed bacteria classification performed well regardless of the size of the training data. We investigated its generalizability not only on the original dataset but also on the few shots (5-shots, 2-shots, and 1-shot) and augmented datasets.

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“…This use of synthetic data can also place constraints on principal component analysis, artificial intelligence, and machine learning models, which correspondingly translates into a large problem of enhanced morbidity, increased healthcare costs, reduced strategies for treatment, and overall public health analysis [31,37,38]. With the limited, accurate resources and synthetic information, it has recently been recommended that the data registered within these online ledgers needs to be (1) findable, (2) accessible, (3) interoperable, and (4) reusable, known as the FAIR Guiding Principles [39].…”

Section: Introductionmentioning

confidence: 99%

BacteSign: Building a Findable, Accessible, Interoperable, and Reusable (FAIR) Database for Universal Bacterial Identification

Childs,

Chand,

Pereira

et al. 2024

Biosensors

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With the increasing incidence of diverse global bacterial outbreaks, it is important to build an immutable decentralized database that can capture regional changes in bacterial resistance with time. Herein, we investigate the use of a rapid 3D printed µbiochamber with a laser-ablated interdigitated electrode developed for biofilm analysis of Pseudomonas aeruginosa, Acinetobacter baumannii, Bacillus subtilis using electrochemical biological impedance spectroscopy (EBIS) across a 48 h spectrum, along with novel ladder-based minimum inhibitory concentration (MIC) sncil tests against oxytetracycline, kanamycin, penicillin G and streptomycin. Furthermore, in this investigation, a search query database has been built demonstrating the deterministic nature of the bacterial strains with real and imaginary impedance, phase, and capacitance, showing increased bacterial specification selectivity in the 9772.37 Hz range.

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

Cited by 26 publications

References 60 publications

Deep learning as a tool for ecology and evolution

Deep learning as a tool for ecology and evolution

A Deep Learning Model for Bacterial Classification Using Big Transfer (BiT)

BacteSign: Building a Findable, Accessible, Interoperable, and Reusable (FAIR) Database for Universal Bacterial Identification

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