Automated Nuclear Cataract Grading in AS-OCT Using Mamba Architecture

Tianxiang Lei, Xiaoming Liu, Ying Zhang, Guohuan Wu, Jinshan Tang

Abstract: Cataract remains one of the leading causes of blindness and visual impairment worldwide, representing a significant public health concern. Anterior segment optical coherence tomography (AS-OCT) provides high-resolution visualization of ocular structures and has become a key imaging modality for nuclear cataract (NC) grading. However, existing convolutional neural network (CNN)-based methods often struggle to differentiate subtle variations between adjacent severity levels due to limited capacity for capturing long-range dependencies, thereby affecting classification accuracy. To address this challenge, we propose an automatic nuclear cataract grading network based on the Mamba architecture. This framework combines the local feature extraction capabilities of traditional CNNs with the long-range dependency modeling power of Mamba modules. Furthermore, we introduce a Hybrid Wavelet Feature Refinement Module (HWFRM), which employs wavelet transforms to extract multi-frequency representations. Integrated with a detail-guided enhancement mechanism, the module adaptively strengthens discriminative features. Channel and spatial attention mechanisms are applied to each wavelet sub-band, enabling the network to selectively emphasize important frequency components and remain sensitive to both structural and fine-detail cues. Finally, an ordinal regression loss is incorporated to explicitly model the progressive nature of cataract severity, improving the network’s ability to reduce misclassifications between adjacent categories. Extensive experiments on both a local AS-OCT dataset and a public benchmark demonstrate that our approach achieves state-of-the-art performance.

External IDs:dblp:conf/smc/LeiLZWT25