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

TL;DR

WaveNet-SF is a hybrid neural network that combines spatial and frequency domain analysis using wavelet transforms to improve retinal disease detection from OCT images, achieving state-of-the-art accuracy.

Contribution

The paper introduces WaveNet-SF, a novel framework integrating wavelet-based multi-scale spatial attention and high-frequency feature compensation for enhanced OCT image analysis.

Findings

Achieves 97.82% accuracy on OCT-C8 dataset.

Achieves 99.58% accuracy on OCT2017 dataset.

Outperforms existing retinal disease detection methods.

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 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.