A Deep Learning and Explainable AI-Based Approach for the Classification of Discomycetes Species
Aras Fahrettin Korkmaz; Fatih Ekinci; Şehmus Altaş; Eda Kumru; Mehmet Serdar Güzel; İlgaz Akata · 2025 · Biology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
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 the most effective model. 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
Abstract by Aras Fahrettin Korkmaz; Fatih Ekinci; Şehmus Altaş; Eda Kumru; Mehmet Serdar Güzel; İlgaz Akata, Biology (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.3390/biology14060719