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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species.

Eda Kumru1, Aras Fahrettin Korkmaz2, Fatih Ekinci3

  • 1Graduate School of Natural and Applied Sciences, Ankara University, 06830 Ankara, Türkiye.

Biology
|October 29, 2025
PubMed
Summary

This study uses deep learning and explainable AI to accurately classify eight similar Earthstar fungal species. The EfficientNet-B3 + DeiT model achieved high accuracy and interpretability, showing potential for agricultural monitoring.

Keywords:
deep learningearthstar fungiensemble modelsexplainable AIfungal classificationmorphological similarity

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Area of Science:

  • Mycology and Computational Biology
  • Artificial Intelligence in Taxonomy

Background:

  • Macroscopic Earthstar fungi (genus Astraeus and Geastrum) exhibit significant morphological similarities, making visual identification challenging.
  • Accurate classification of these species is crucial for ecological studies and understanding fungal biodiversity.

Purpose of the Study:

  • To develop and evaluate deep learning models for the multi-class, image-based classification of eight morphologically similar Earthstar fungal species.
  • To enhance model interpretability using explainable artificial intelligence (XAI) techniques.
  • To introduce novel hybrid ensemble models for improved classification stability and accuracy.

Main Methods:

  • Utilized eight deep learning architectures (CNNs and transformers) including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet.
  • Employed Grad-CAM and Score-CAM for visualizing classification decision rationale.
  • Designed and evaluated two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S.

Main Results:

  • Individual model accuracy ranged from 86.16% to 96.23%, with EfficientNet-B3 performing best.
  • Ensemble models achieved improved stability, with EfficientNet-B3 + DeiT reaching 93.71% and DenseNet121 + MaxViT-S reaching 93.08% accuracy.
  • The EfficientNet-B3 + DeiT ensemble demonstrated the most balanced performance with 93.83% precision, 93.72% recall, and an MCC of 0.9282.
  • XAI techniques provided biologically meaningful insights into classification decisions.

Conclusions:

  • The proposed deep learning and XAI framework successfully classifies morphologically similar Earthstar fungi with high accuracy and interpretability.
  • Hybrid ensemble models, particularly EfficientNet-B3 + DeiT, offer robust and stable classification performance.
  • This approach has potential applications in monitoring symbiotic fungi in agricultural ecosystems and promoting sustainable practices.