Research Article: SkinDet-YOLO: a context- and boundary-aware yolov8-based framework for skin disease detection
Abstract:
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.
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).
SkinDet-YOLO achieves validation [email protected] of 0.992 and [email protected]:0.95 of 0.900, outperforming strong baselines under a 7:2:1 train/validation/test split.
Ablation and sensitivity analyses show complementary gains from CAMF and ABADH and support the framework's potential for clinical skin lesion detection.
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.
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