RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection

• URL: http://arxiv.org/abs/2608.17623v1
• Date: Tue, 18 Aug 2026 10:38:59 GMT
• Title: RetiWave-Mamba: A Dual-Stream Network for Retinal Disease Detection based on Multi-scale Context and Frequency-Adaptive Mamba Projection
• Abstract summary: We propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models.<n>Our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.25%, surpassing existing methods.
• Score: 11.498068680313613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Frequency-Adaptive Mamba Projector (FAMP) is incorporated to capture long-range dependencies within disjoint high-frequency textural features. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.25%, surpassing existing methods. These results highlight the efficacy of RetiWave-Mamba in robustly identifying retinal pathologies under noisy conditions, offering a promising tool for clinical diagnosis.

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