Deep learning approach to describe and classify fungi microscopic images

Zieliński, Bartosz; Sroka-Oleksiak, Agnieszka; Rymarczyk, Dawid; Piekarczyk, Adam; Brzychczy-Włoch, Monika

doi:10.1371/journal.pone.0234806

Cited by 47 publications

(37 citation statements)

References 25 publications

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“…To further recognize the fungi, template matching method and concave point detection method were employed to determine circles and concave points respectively. Zielinski et al [ 122 ] proposed a DL based technique for the microscopic image classification of fungal species. The experiment was conducted on 180 images of five fungal species.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani

Kotwal

Manhas

et al. 2021

Arch Computat Methods Eng

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Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

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Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani

Kotwal

Manhas

et al. 2021

Arch Computat Methods Eng

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“…One article described the use of quantitative phase spectroscopy to detect P. falciparum but used one single strain [40]. Only two ML systems focused on mycology, both using very limited datasets to identify Candida species on culture [41,42].…”

Section: Microorganism Detection Identification and Quantificationmentioning

confidence: 99%

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Peiffer-Smadja

Dellière

Rodriguez

et al. 2020

Clinical Microbiology and Infection

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Background: Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems. Aims: This narrative review aims to explore the current use of ML In clinical microbiology. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019. Content: We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n ¼ 71, 73%) but a significant number used data from low-and middle-income countries (n ¼ 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes. Implications: In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.

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“…In addition to bacterial images, a model based on deep neural networks and bag-ofwords approaches was able to classify microscopic images of a diverse range of fungal species (e.g. Candida spp., Saccharomyces spp., and Malassezia furfur) with a maximum total accuracy of 93.9% [88].…”

Section: Detection and Identification Of Microorganismsmentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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Deep learning approach to describe and classify fungi microscopic images

Cited by 47 publications

References 25 publications

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

Recent evolutions of machine learning applications in clinical laboratory medicine

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