MASA-Net: Multi-Aspect Channel–Spatial Attention Network With Cross-Layer Feature Aggregation for Accurate Fungi Species Identification

Abstract

Accurate identification of fungal species is essential for effective diagnosis and treatment. Traditional microscopy-based methods are often subjective and time-consuming. Deep learning has emerged as a promising tool in this domain. However, existing deep learning models often struggle to generalise in the presence of class imbalance and subtle morphological differences, which are common in fungal image datasets. This study proposes MASA-Net, a deep learning framework that combines a fine-tuned DenseNet201 backbone with a multi-aspect channel–spatial attention (MASA) module. The attention mechanism refines spatial and channel-wise features by capturing multi-scale spatial patterns and adaptively emphasising informative channels. This enhances the network's ability to focus on diagnostically relevant fungal structures while suppressing irrelevant features. The MASA-Net is evaluated on the DeFungi dataset and demonstrates superior performance in terms of accuracy, precision, recall and F1-score. It also outperforms established attention mechanisms such as squeeze-and-excitation networks (SE) and convolutional block attention module (CBAM) under identical conditions. These results highlight MASA-Net's robustness and effectiveness in addressing class imbalance and structural variability, offering a reliable solution for automated fungal species identification.