EADC-Net: A Dual-Stage Deep Learning Framework with Spatial and Channel Central Attention for Automated Detection of Lumbar Degenerative Disc Disease from Radiographs.

Lumbar degenerative disc disease (DDD) is a primary driver of chronic back pain and spinal deformity; however, MRI-based diagnosis is constrained by high costs and limited accessibility. We introduce EADC-Net, an adaptive deep learning framework for automated DDD detection from standard radiographs to facilitate early clinical screening. EADC-Net utilizes a dual-stage architecture. Lumbar regions of interest are initially extracted via segmentation deep residual neural network (SRNet), followed by contrast-limited adaptive histogram equalization (CLAHE) contrast enhancement. To account for disc height interdependencies, images are partitioned into overlapping segments (L1-L3, L2-L4, and L3-L5). The detection engine, built on a VGG-based backbone, incorporates a novel spatial and channel central attention (SCCA) module. This module synergistically leverages global weighted average pooling (GWAP) and coordinate attention (CA) to prioritize features in the nucleus pulposus and disc boundaries. Evaluated on 402 radiographs from 204 patients under a rigorous patient-level fivefold cross-validation framework, EADC-Net achieved an average accuracy of 86.26%, recall of 79.14%, precision of 81.00%, and an F1-score of 79.79%. Comparative analysis and ablation studies confirmed that EADC-Net significantly outperforms benchmark architectures by effectively capturing subtle degenerative features. EADC-Net offers a reliable, cost-effective tool for automated DDD detection. By bridging the gap between radiographic imaging and high-accuracy deep learning, this framework supports early spinal health management.