FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns
Fungi play crucial roles in many ecosystems; however, traditional identification methods are often time- and labor-intensive. In this study, we introduce FungID, a pilot and novel deep learning algorithm, alongside its user-friendly software implementation, developed by analyzing various fungal spec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Pathogens |
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| Online Access: | https://www.mdpi.com/2076-0817/14/3/242 |
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| author | John Pouris Konstantinos Konstantinidis Ioanna Pyrri Effie G. Papageorgiou Chrysa Voyiatzaki |
| author_facet | John Pouris Konstantinos Konstantinidis Ioanna Pyrri Effie G. Papageorgiou Chrysa Voyiatzaki |
| author_sort | John Pouris |
| collection | DOAJ |
| description | Fungi play crucial roles in many ecosystems; however, traditional identification methods are often time- and labor-intensive. In this study, we introduce FungID, a pilot and novel deep learning algorithm, alongside its user-friendly software implementation, developed by analyzing various fungal species for identification based on chromogenic profiling of colony color patterns via a Convolutional Neural Network. Training and testing FungID upon a set of 269 images showed remarkable performance in terms of model robustness and classification efficacy. These findings demonstrate that FungID offers a potential method for rapid and reliable identification of fungal species through chromogenic profiling, providing additional tools to conventional techniques being employed in the fields of health, microbiology, biotechnology, and more. Our research underscores the promising role of deep learning algorithms in enhancing the understanding of the taxonomy and ecological functions of fungi that can be grown in pure cultures, while also emphasizing the importance of carefully assessing the scope and limitations of these methods. |
| format | Article |
| id | doaj-art-4cf2ce49d5a84c15b391107e3c51c344 |
| institution | OA Journals |
| issn | 2076-0817 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pathogens |
| spelling | doaj-art-4cf2ce49d5a84c15b391107e3c51c3442025-08-20T01:48:57ZengMDPI AGPathogens2076-08172025-03-0114324210.3390/pathogens14030242FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color PatternsJohn Pouris0Konstantinos Konstantinidis1Ioanna Pyrri2Effie G. Papageorgiou3Chrysa Voyiatzaki4Laboratory of Molecular Microbiology and Immunology, Department of Biomedical Sciences, University of West Attica, 12243 Athens, GreeceLaboratory of Biology, Department of Medicine, Democritus University of Thrace, Dragana, 68100 Alexandroupolis, GreeceDepartment of Ecology and Systematics, Faculty of Biology, University of Athens, Panepistimioupoli, 15784 Athens, GreeceLaboratory of Reliability and Quality Control in Laboratory Hematology (HemQcR), Department of Biomedical Sciences, School of Health and Care Sciences, University of West Attica, 12243 Athens, GreeceLaboratory of Molecular Microbiology and Immunology, Department of Biomedical Sciences, University of West Attica, 12243 Athens, GreeceFungi play crucial roles in many ecosystems; however, traditional identification methods are often time- and labor-intensive. In this study, we introduce FungID, a pilot and novel deep learning algorithm, alongside its user-friendly software implementation, developed by analyzing various fungal species for identification based on chromogenic profiling of colony color patterns via a Convolutional Neural Network. Training and testing FungID upon a set of 269 images showed remarkable performance in terms of model robustness and classification efficacy. These findings demonstrate that FungID offers a potential method for rapid and reliable identification of fungal species through chromogenic profiling, providing additional tools to conventional techniques being employed in the fields of health, microbiology, biotechnology, and more. Our research underscores the promising role of deep learning algorithms in enhancing the understanding of the taxonomy and ecological functions of fungi that can be grown in pure cultures, while also emphasizing the importance of carefully assessing the scope and limitations of these methods.https://www.mdpi.com/2076-0817/14/3/242chromogeniccolor patternfungiFungIDfungi identification |
| spellingShingle | John Pouris Konstantinos Konstantinidis Ioanna Pyrri Effie G. Papageorgiou Chrysa Voyiatzaki FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns Pathogens chromogenic color pattern fungi FungID fungi identification |
| title | FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns |
| title_full | FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns |
| title_fullStr | FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns |
| title_full_unstemmed | FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns |
| title_short | FungID: Innovative Fungi Identification Method with Chromogenic Profiling of Colony Color Patterns |
| title_sort | fungid innovative fungi identification method with chromogenic profiling of colony color patterns |
| topic | chromogenic color pattern fungi FungID fungi identification |
| url | https://www.mdpi.com/2076-0817/14/3/242 |
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