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This study proposes a deep learning-based classification framework for six macrofungi species using advanced convolutional neural network (CNN) architectures. Among the tested models, DenseNet121 achieved a 90% F1-score, while EfficientNetV2-M reached 92% accuracy and 92% precision. AUC scores of 90% or higher were observed for DenseNet, EfficientNet-B4, EfficientNetV2-M, and ShuffleNet, demonstrating robust classification capabilities. Importantly, this study pioneers the integration of explainable artificial intelligence (XAI) in macrofungi classification by employing Grad-CAM. These visualizations revealed that top-performing models focused on biologically relevant features such as the cap, stem, and spore surfaces, whereas less accurate models fixated on background areas, leading to misclassification. 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