A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species

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Korkmaz A. F., Ekinci F., Altaş Ş., Kumru E., Güzel M. S., Akata I.

BIOLOGY, vol.14, no.6, pp.719-748, 2025 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 14 Issue: 6
  • Publication Date: 2025
  • Doi Number: 10.3390/biology14060719
  • Journal Name: BIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Page Numbers: pp.719-748
  • Open Archive Collection: AVESIS Open Access Collection
  • Istanbul Kültür University Affiliated: Yes

Abstract

This study presents a novel approach for classifying Discomycetes species using deep

learning and explainable artificial intelligence (XAI) techniques. The EfficientNet-B0 model

achieved the highest performance, reaching 97% accuracy, a 97% F1-score, and a 99% AUC,

making it themost effectivemodel. MobileNetV3-L followed closely, with 96% accuracy, a 96%

F1-score, and a 99% AUC, while ShuffleNet also showed strong results, reaching 95% accuracy

and a 95% F1-score. In contrast, the EfficientNet-B4 model exhibited lower performance, achieving

89% accuracy, an 89% F1-score, and a 93% AUC. These results highlight the superior feature

extraction and classification capabilities of EfficientNet-B0 andMobileNetV3-L for biological

data. Explainable AI (XAI) techniques, including Grad-CAMand Score-CAM, enhanced the

interpretability and transparency ofmodel decisions. Thesemethods offered insights into the

internal decision-making processes of deep learning models, ensuring reliable classification

results. This approach improves traditional taxonomy by advancing data processing and supporting

accurate species differentiation. In the future, using larger datasets and more advanced

AI models is recommended for biodiversity monitoring, ecosystem modeling, medical imaging,

and bioinformatics. Beyond high classification performance, this study offers an ecologically

meaningful approach by supporting biodiversity conservation and the accurate identification

of fungal species. These findings contribute to developingmore precise and reliable biological

classification systems, setting new standards for AI-driven research in biological sciences.