Dual-Attention EfficientNet Hybrid U-Net for Segmentation of Rheumatoid Arthritis Hand X-Rays

<b>Background</b>: Accurate segmentation in radiographic imaging remains difficult due to heterogeneous contrast, acquisition artifacts, and fine-scale anatomical boundaries. <b>Objective</b>: This paper presents a Hybrid Attention U-Net, which paired an EfficientNet-B3 encoder with a decoder that is both lightweight, featuring CBAM and SCSE modules, and complementary for channel-wise and spatial-wise recalibration of sharper boundary recovery. <b>Methods</b>: The preprocessing phase uses percentile windowing, N4 bias compensation, per-image normalization, and geometric standardization as well as sparse geometric augmentations to reduce domain shift and make the pipeline viable. <b>Results</b>: For hand X-ray segmentation, the model achieves results with Dice = 0.8426, IoU around 0.78, pixel accuracy = 0.9058, ROC-AUC = 0.9074, and PR-AUC = 0.8452, and converges quickly at the early stages and remains steady at late epochs. Controlled ablation shows that the main factor of overlap quality of EfficientNet-B3 and that smaller batches (bs = 16) are always better at gradient noise and implicit regularization than larger batches. The qualitative overlays are complementary to quantitative gains that reveal more distinct cortical profiles and lower background leakage. <b>Conclusions</b>: It is computationally moderate, end-to-end trainable, and can be easily extended to multi-class problems through a softmax head and class-balanced objectives, rendering it a powerful, deployable option for musculoskeletal radiograph segmentation as well as an effective baseline in future clinical translation analyses.