ArticleFrontiers in oncology2026
SkinDet-YOLO: a context- and boundary-aware yolov8-based framework for skin disease detection.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Skin diseases are among the most common human disorders, and accurate localization of suspicious lesions in clinical or dermoscopic images is a critical first step towards computer-aided diagnosis. However, skin lesion detection remains challenging due to extreme scale variation, fuzzy lesion boundaries, and high inter-class visual similarity. Methods: We propose SkinDet-YOLO, a YOLOv8-based framework with a Context-Aware Multi-scale Feature Fusion network (CAMF) and an Adaptive Boundary-Aware Detection Head (ABADH). Results: SkinDet-YOLO achieves validation mAP@0.5 of 0.992 and mAP@0.5:0.95 of 0.900, outperforming strong baselines under a 7:2:1 train/validation/test split. Discussion: Ablation and sensitivity analyses show complementary gains from CAMF and ABADH and support the framework's potential for clinical skin lesion detection.
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