ArticleDigital health
SCC-Net: A lightweight attention-enhanced deep learning model for automated squamous cell carcinoma detection.
Article in Digital health. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
3 authors.
Funding
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Abstract
Objectives: The primary objective of this study was to develop and validate SCC-Net, a lightweight GhostNet-CBAM model, for the early and accurate detection of cutaneous squamous cell carcinoma (cSCC) from dermoscopy images, addressing the limitations of resource-constrained clinical settings. Methods: We developed SCC-Net, a lightweight GhostNet-CBAM model trained on 5150 public dermoscopy images (628 cSCC) formulated as a binary classification task (cSCC vs. non-cSCC). Using five-fold cross-validation and external testing against five CNN baselines, evaluating accuracy, recall, F1, AUC, and Grad-CAM interpretability. Results: SCC-Net achieved 96.6% accuracy, 88.7% recall, and 0.986 AUC internally, and 94.3% accuracy, 93.8% recall, and 0.986 AUC externally, outperforming all comparables with only 1.59 M parameters. Grad-CAM heatmaps aligned with expert lesion focus. Conclusion: SCC-Net delivers accurate, explainable, and low-cost cSCC detection, promising for teledermatology and resource-limited settings.
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Registered trials
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