A Novel ConvNeXtV2–MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology.
Fungal infections, especially in people with weakened immune systems, are a significant global health burden. Accurate identification of fungal morphology from microscopic images is a critical step in guiding timely antifungal treatment decisions. However, manual morphological assessment remains hig...
| Publicado en: | Journal of Imaging Informatics in Medicine pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194746415&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194746415 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194746415 10.1007/s10278-026-02052-5 194746415 ppf: 1 ppct: 11 formats: tig: atl: A Novel ConvNeXtV2–MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology. aug: au: Ari, Nuray Erten, Mehmet Kayali, Sümeyra Özdemir, Esra Yüzgeç Koç, Canan Özyurt, Fatih affil: Department of Medical Microbiology, Faculty of Medicine, Firat University sug: ab: Fungal infections, especially in people with weakened immune systems, are a significant global health burden. Accurate identification of fungal morphology from microscopic images is a critical step in guiding timely antifungal treatment decisions. However, manual morphological assessment remains highly dependent on expert mycologists and is prone to inter-observer variability. In this study, we propose a hybrid deep learning framework that integrates the ConvNeXtV2-Base architecture with a Multi-Head Attention–based Multiple Instance Learning module for automated classification of microscopic fungal morphology images. The framework was evaluated on the open-access DeFungi dataset, consisting of 3696 microscopic images representing five clinically meaningful fungal morphology classes. In comparative experiments, classical vision transformer (ViT) models achieved 91.20% accuracy, while MIL-enhanced ViT models reached 93.99%. The proposed ConvNeXtV2-Base + MIL hybrid method outperformed all evaluated architectures, achieving 98.90% classification accuracy. These results establish a new benchmark for automated fungal morphology classification and highlight the potential of AI-assisted decision-support tools to aid expert mycologists in morphology-based assessment workflows. pubtype: Academic Journal doctype: Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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