ArticleJournal of pathology informatics2023
Classification of fungal genera from microscopic images using artificial intelligence.
Article in Journal of pathology informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.Fungal biology and biotechnology · 2026Article
- Explainable deep learning techniques for microscopic fungi classification using a learnable threshold-based ReLULeaky activation function and transfer learning.Journal of pathology informatics · 2026Article
- Conditional superiorities and unaddressed bottlenecks: a critical review of artificial intelligence for waterborne microbial detection.Applied microbiology and biotechnology · 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
- Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Molecular Identification and RNA-Based Management of Fungal Plant Pathogens: From PCR to CRISPR/Cas9.International journal of molecular sciences · 2026Review
- Review
- Deep learning application to hyphae and spores identification in fungal fluorescence images.Scientific reports · 2025Article
- OpenFungi: A Machine Learning Dataset for Fungal Image Recognition Tasks.Life (Basel, Switzerland) · 2025Article
- Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.Sensors (Basel, Switzerland) · 2024Article
- Exploring Fungal Diversity in Seagrass Ecosystems for Pharmaceutical and Ecological Insights.Journal of fungi (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopic image examination is fundamental to clinical microbiology and often used as the first step to diagnose fungal infections. In this study, we present classification of pathogenic fungi from microscopic images using deep convolutional neural networks (CNN). We trained well-known CNN architectures such as DenseNet, Inception ResNet, InceptionV3, Xception, ResNet50, VGG16, and VGG19 to identify fungal species, and compared their performances. We collected 1079 images of 89 fungi genera and split our data into training, validation, and test datasets by 7:1:2 ratio. The DenseNet CNN model provided the best performance among other CNN architectures with overall accuracy of 65.35% for top 1 prediction and 75.19% accuracy for top 3 predictions for classification of 89 genera. The performance is further improved (>80%) after excluding rare genera with low sample occurrence and applying data augmentation techniques. For some particular fungal genera, we obtained 100% prediction accuracy. In summary, we present a deep learning approach that shows promising results in prediction of filamentous fungi identification from culture, which could be used to enhance diagnostic accuracy and decrease turnaround time to identification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.