ArticleFrontiers in medicine2026
FD-YOLO-Skin: frequency-domain enhanced YOLO for single-class skin lesion detection.
Article in Frontiers in medicine, 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: Automatic detection of skin lesions in dermoscopic images remains challenging due to large intra-class variation, low-contrast boundaries, and severe foreground-background imbalance. Methods: We propose FD-YOLO-Skin, a frequency-domain enhanced YOLOv8 framework for single-class Results: On the ISIC-Style Micronucleus Lesion Detection Benchmark (ISIC-MLD; 10,015 de-identified dermoscopic images), FD-YOLO-Skin achieves an mAP@0.5 of 0.990 ± 0.003 (95% CI: [0.986, 0.994]) and an mAP@0.5:0.95 of 0.905 ± 0.006 on the held-out test split, with precision and recall above 0.97. Ablations show that FMSFF mainly improves recall for small or low-contrast lesions, whereas FDCL reduces false positives and improves precision relative to aggressive spatial-domain augmentation alone. Discussion: Explicit frequency-domain multi-scale fusion and contrastive regularization improve single-class skin lesion detection with modest computational overhead. Source code, preprocessed dataset splits, and model weights are available at https://anonymous.4open.science/r/skin2-B816/.
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