ArticlePloS one2020
Deep learning approach to describe and classify fungi microscopic images.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- The future of fungal diagnostics: What's next in laboratory testing for invasive fungal infection?Medical mycology · 2026Review
- Explainable deep learning techniques for microscopic fungi classification using a learnable threshold-based ReLULeaky activation function and transfer learning.Journal of pathology informatics · 2026Article
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.Scientific reports · 2026Article
- Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.Microbiology spectrum · 2026Article
- Macroscopic aspergillus recognition using YOLO-CSM.PloS one · 2026Article
- Deep learning classification of INSV-associated weeds in Monterey county using a curated RGB image dataset.Scientific reports · 2025Article
- MACNeXt-Based Bacteria Species Detection.Microorganisms · 2025Article
- Deep learning application to hyphae and spores identification in fungal fluorescence images.Scientific reports · 2025Article
- Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.Medical sciences (Basel, Switzerland) · 2025Review
- Detection and Identification of Food-Borne Yeasts: An Overview of the Relevant Methods and Their Evolution.Microorganisms · 2025Review
- Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?Clinical, cosmetic and investigational dermatology · 2025Review
- Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.Scientific reports · 2024Article
- Deep learning-based characterization of neutrophil activation phenotypes inComputational and structural biotechnology journal · 2024Article
- Label-Free Optical Transmission Tomography for Direct Mycological Examination and Monitoring of Intracellular Dynamics.Journal of fungi (Basel, Switzerland) · 2024Article
- Article
- Diagnosis of invasive fungal infections: challenges and recent developments.Journal of biomedical science · 2023Review
- Attention-Guided Transfer Learning for Identification of Filamentous Fungi Encountered in the Clinical Laboratory.Microbiology spectrum · 2023Article
- Plant Parasitic Nematode Identification in Complex Samples with Deep Learning.Journal of nematology · 2023Article
- Exploring the application of machine learning to expert evaluation of research impact.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.
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