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 the 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 (HFFC) block 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.

翻译：视网膜疾病是导致视力损伤和失明的主要原因，及时诊断对于有效治疗至关重要。光学相干断层扫描（OCT）已成为视网膜疾病诊断的标准成像方式，但OCT图像常受散斑噪声、复杂病灶形态和病灶尺寸多变等问题影响，使得判读具有挑战性。本文提出一种新颖的框架WaveNet-SF，通过整合空间域与频域学习来增强视网膜疾病检测。该框架利用小波变换将OCT图像分解为低频与高频分量，使模型能够同时提取全局结构特征和细粒度细节。为提升病灶检测能力，我们引入了多尺度小波空间注意力（MSW-SA）模块，该模块增强了模型对多尺度感兴趣区域的关注。此外，还融合了高频特征补偿（HFFC）模块，以恢复小波分解过程中丢失的边缘信息、抑制噪声，并保留对病灶检测至关重要的细微细节。我们的方法在OCT-C8和OCT2017数据集上分别达到了97.82%和99.58%的最先进（SOTA）分类准确率，超越了现有方法。这些结果证明了WaveNet-SF在应对OCT图像分析挑战方面的有效性及其作为视网膜疾病诊断强大工具的潜力。