PDC-DETR: a Parallel Dilated Convolutional Detection Transformer for preliminary screening of multiple macular degeneration

Current diagnostic approaches for macular degeneration in optical coherence tomography (OCT) images often lead to misdiagnosis due to their limited ability to capture the disease’s multiscale and irregular features. We present PDC-DETR, a Parallel Dilated Convolutional Detection Transformer, which introduces three major innovations for accurate and efficient macular degeneration analysis. First, a Parallel Feature-Optimized Attention Pyramid Network (PFOAPN) enables simultaneous modeling of global context and local details through multi-scale feature pathways. Second, a novel Wise-MPDIoU loss dynamically adjusts to variations in image quality while improving lesion localization accuracy. Third, the lightweight design ensures real-time clinical applicability, requiring only 38.2 MB parameters and 58.5 GFLOPs, while achieving 94.1% accuracy across five macular degeneration categories at 71 FPS. This study establishes a new benchmark for automated OCT-based retinal disease detection, providing both high diagnostic accuracy and practical clinical deployment potential.