ERYXSeg: a hybrid CNN architecture for robust and resource-aware wound segmentation

In modern clinical practice, the accuracy of wound segmentation is important because it allows for data-driven treatment planning, accurate evaluation of the wound area, and tracking of the healing process. In this paper, we introduce ERYXSeg, an integrated deep learning architecture that combines residual skip connections, a parameter-efficient core, and spatial localization and boundary-aware refinement of a segmentation head. The proposed method achieves robust performance across a wide range of wound types by capturing both local fine-grained characteristics and rich contextual information. ERYXSeg outperforms the current state-of-the-art models under identical training settings after being trained from scratch on mixed wound datasets. It achieves the highest reported IoU and Dice scores in both datasets: 0.7633 IoU and 0.8658 Dice for the foot ulcer dataset and 0.6910 IoU and 0.8173 Dice for the curated mixed wound dataset. The contribution of each architectural element is further validated by a systematic ablation study, which shows that attention-gated skip connections are crucial for accurate spatial reconstruction across various data sources and that Mobile Inverted Bottleneck Convolution (MBConv) blocks are essential for extracting broadly applicable features. The results obtained demonstrate excellent generalization and high computational efficiency of the model, which thus qualifies it for real-time clinical deployment independent of transfer learning.