Patient-Aware Sequential Partition Learning with a Progressive Sparse Mixture-of-Experts Architecture for Imbalanced Retinal OCT Classification

Retinal optical coherence tomography (OCT) classification is often affected by substantial class imbalance and by evaluation bias when images from the same patient appear in both training and test sets. This study proposes MCSP-LightP3R-MoE, a patient-aware framework that combines minority-guided cyclic sequential partition learning with progressive lightweight sparse Mixture-of-Experts routing for four-class retinal OCT classification. Group-disjoint training, validation, and test subsets were constructed from 84,484 OCT images covering CNV, DME, DRUSEN, and NORMAL classes. The model uses an ImageNet-pretrained ResNet18 backbone with three progressive LightMoE modules, heterogeneous lightweight experts, and Top-1 routing, and was evaluated over three independent seeds against conventional CNN, imbalance-handling, and ablation baselines. MCSP-LightP3R-MoE achieved 93.28 ± 0.29% balanced accuracy and 92.06 ± 0.37% macro-F1, while DRUSEN sensitivity reached 89.17 ± 1.92%, an 8.78 percentage-point improvement over ResNet18. Patient-cluster bootstrap analysis supported several gains in balanced accuracy and minority-class sensitivity relative to conventional CNN baselines. The model contains 11.57 million parameters, only 3.47% more than ResNet18. These results indicate that patient-aware sequential exposure and sparse expert routing can improve minority-sensitive OCT classification while retaining a single compact inference model.