Advanced deep learning approaches for the accurate classification of Phallaceae fungi with explainable AI

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Kumru E., Ekinci F., Açıcı K., Altındal Ö. B., Güzel M. S., Akata I.

TURKISH JOURNAL OF BOTANY, cilt.49, ss.1-18, 2025 (SCI-Expanded)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 49
• Basım Tarihi: 2025
• Dergi Adı: TURKISH JOURNAL OF BOTANY
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Geobase, Veterinary Science Database
• Sayfa Sayıları: ss.1-18
• Ankara Üniversitesi Adresli: Evet

Özet

In this study, deep learning (DL)-based models were developed for the classification of 5 fungal species from the Phallaceae

family (Clathrus ruber, Colus hirudinosus, Mutinus caninus, Phallus impudicus, and Pseudocolus fusiformis). ConvNeXT achieved the

highest performance with 98% accuracy, 98% precision, 98% recall, and 98% F1-score. EfficientNetB4 and Xception also performed

well with 96% accuracy. In contrast, lighter models such as MobileNetV2 and MixNet S showed significantly lower accuracy (84%

and 80%, respectively). Among the explainable artificial intelligence (XAI) techniques, gradient-weighted class activation mapping

(Grad-CAM) and Integrated Gradients showed that high-accuracy models focus more effectively on biologically meaningful regions.

In particular, the ConvNeXT plus Grad-CAM combination consistently highlighted critical structural areas, such as the cap and stalk

of fungi, resulting in more accurate classifications. These findings show that DL-based models offer high accuracy in classifying fungal

species with complex morphological features. Furthermore, XAI techniques play a critical role in enhancing classification processes.