WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in the Spatial-Frequency Domain

2025-01-21

AI Summary

Early diagnosis of retinal diseases from OCT images remains challenging due to severe speckle noise, complex lesion morphology, and highly variable lesion scales. To address these issues, this paper proposes a spatial-frequency joint learning framework. Its core contributions are: (1) a novel multi-scale wavelet spatial-attention (MSW-SA) module that enhances lesion region localization; and (2) a high-frequency feature compensation (HFFC) block that effectively restores edge details while suppressing speckle noise. By integrating wavelet transform, multi-scale feature modeling, and dual-domain (spatial and frequency) attention mechanisms, the method achieves state-of-the-art classification accuracy of 97.82% on OCT-C8 and 99.58% on OCT2017—surpassing prior methods on both benchmarks. The framework demonstrates significantly improved robustness to noise and superior fine-grained discriminative capability for subtle pathological patterns.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coherence Tomography (OCT) has become a standard imaging modality for retinal disease diagnosis, but OCT images often suffer from issues such as speckle noise, complex lesion shapes, and varying lesion sizes, making interpretation challenging. In this paper, we propose a novel framework, WaveNet-SF, to enhance retinal disease detection by integrating spatial-domain and frequency-domain learning. The framework utilizes wavelet transforms to decompose OCT images into low- and high-frequency components, enabling the model to extract both global structural features and fine-grained details. To improve lesion detection, we introduce a multi-scale wavelet spatial attention (MSW-SA) module, which enhances the model's focus on regions of interest at multiple scales. Additionally, a high-frequency feature compensation block (HFFC) is incorporated to recover edge information lost during wavelet decomposition, suppress noise, and preserve fine details crucial for lesion detection. Our approach achieves state-of-the-art (SOTA) classification accuracies of 97.82% and 99. 58% on the OCT-C8 and OCT2017 datasets, respectively, surpassing existing methods. These results demonstrate the efficacy of WaveNet-SF in addressing the challenges of OCT image analysis and its potential as a powerful tool for retinal disease diagnosis.

Problem

• Retinal Disease Diagnosis
• Optical Coherence Tomography (OCT) Imaging
• Noise and Variability in Lesion Characteristics

Innovation

• WaveNet-SF
• Multi-scale Wavelet Spatial Attention
• High-frequency Feature Compensation