WHA-Net: A Low-Complexity Hybrid Model for Accurate Pseudopapilledema Classification in Fundus Images

The fundus manifestations of pseudopapilledema closely resemble those of optic disc edema, making their differentiation particularly challenging in certain clinical situations. However, rapid and accurate diagnosis is crucial for alleviating patient anxiety and guiding treatment strategies. This study proposes an efficient low-complexity hybrid model, WHA-Net, which innovatively integrates three core modules to achieve precise auxiliary diagnosis of pseudopapilledema. First, the wavelet convolution (WTC) block is introduced to enhance the model’s characterization capability for vessel and optic disc edge details in fundus images through 2D wavelet transform and deep convolution. Additionally, the hybrid attention inverted residual (HAIR) block is incorporated to extract critical features such as vascular morphology, hemorrhages, and exudates. Finally, the Agent-MViT module effectively captures the continuity features of optic disc contours and retinal vessels in fundus images while reducing the computational complexity of traditional Transformers. The model was trained and evaluated on a dataset of 1793 rigorously curated fundus images, comprising 895 normal optic discs, 485 optic disc edema (ODE), and 413 pseudopapilledema (PPE) cases. On the test set, the model achieved outstanding performance, with 97.79% accuracy, 95.55% precision, 95.69% recall, and 98.53% specificity. Comparative experiments confirm the superiority of WHA-Net in classification tasks, while ablation studies validate the effectiveness and rationality of each module’s combined design. This research provides a clinically valuable solution for the automated differential diagnosis of pseudopapilledema, with both computational efficiency and diagnostic reliability.