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<p>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.</p>

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A Novel ConvNeXtV2–MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Nuray Ari,
  • Mehmet Erten,
  • Sümeyra Kayali,
  • Esra Yüzgeç Özdemir,
  • Canan Koç,
  • Fatih Özyurt

摘要

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.