ArticleJournal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine2026
Deep Learning-Based Skin Lesion Classification: A CNN Approach on High-Frequency Ultrasound Imaging.
Article in Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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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Who cites it
2 citing papers in PubMed.
- Article
- Methodological Considerations for Improving Deep Learning-Based Classification of Skin Lesions in High-Frequency Ultrasound Imaging.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
High-frequency ultrasound (HFUS) is valuable for assessing skin lesions, supporting diagnosis, treatment monitoring, and surgical planning. This study evaluates deep learning models for binary classification of HFUS images acquired in B-mode and Doppler mode. Two single-input CNNs were trained with each modality, while Unity and Cascade architectures combined both. The HFUS-Doppler model achieved the best performance (95.0% accuracy, AUC 0.98), followed by Unity (90.5% accuracy, AUC 0.97). Cascade showed lower accuracy but greater confidence in malignant predictions. Probability distribution analysis revealed differences in model certainty near the decision threshold. Results indicate that combining B-mode and Doppler can enhance diagnostic performance, depending on network design and data quality, supporting the potential of customized deep learning for non-invasive HFUS-based skin lesion classification.
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Registered trials
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