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

• URL: http://arxiv.org/abs/2501.11854v1
• Date: Tue, 21 Jan 2025 03:10:52 GMT
• Title: WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on Wavelet Transform in the Spatial-Frequency Domain
• Authors: Jilan Cheng, Guoli Long, Zeyu Zhang, Zhenjia Qi, Hanyu Wang, Libin Lu, Shuihua Wang, Yudong Zhang, Jin Hong,
• Abstract summary: 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. Our approach achieves state-of-the-art (SOTA) classification accuracies of 97.82% and 99. 58% on the OCT-C8 and OCT 2017 datasets, respectively.
• Score: 15.21932120859184
• License:
• 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.

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