Lower limb and feet wound image dataset.

A comprehensive wound-related image repository was developed to address critical gaps in existing medical imaging resources, particularly the lack of balanced datasets representing both healthy and pathological lower-limb conditions. The collection comprises 5443 images sourced from two complementary streams: real-world clinical wound cases and controlled acquisition of healthy feet images. The wound component includes 2686 expertly annotated images representing eight clinically significant wound types-diabetic, pressure, trauma, venous, surgical, arterial, cellulitis, and miscellaneous categories. These images were gathered across diverse clinical environments between 2015 and 2019 and meticulously annotated by certified wound specialists, ensuring high-quality segmentation masks including peri‑wound regions. The healthy-foot component consists of 2757 images captured from volunteer participants in naturalistic settings using consumer-grade smartphone cameras. Each participant contributed eight multi-angle images under consistent protocols, enabling robust representation of anatomical variability across sex, skin tone, and foot structure. All images were standardized through controlled resizing procedures, while the wound dataset underwent additional mask generation and augmentation strategies to support downstream segmentation and classification tasks. This unified dataset provides a balanced foundation for developing machine learning models capable of distinguishing between normal and pathological foot conditions while supporting advanced tasks such as wound segmentation, severity assessment, and clinical decision support. By integrating healthy and wound images within a single accessible collection, the dataset mitigates class imbalance issues prevalent in existing resources and enables scalable, generalizable deep learning research in wound detection, monitoring, and medical image analysis.