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Warning Signs of Melanoma (ABCDE Rule)
Primary visual criteria for self-assessing suspicious skin spots.
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Multi-scale and edge-aware IFGNet for precise skin lesion ... - Nature
Article Open access Published: 01 May 2026 Bo Li1, Peiwen Tan1, Jie Jia1 & …Xinyan Chen1 Scientific Reports , Article number: (2026) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply. AbstractAccurate segmentation of dermoscopic images is essential for early melanoma diagnosis, yet current methods remain limited. CNN-based models capture local details but lack global context, whereas Transformer-based approaches model long-range dependencies but often lose fine structures; both struggle with blurred boundaries and scale variations. To address these challenges, we propose IFGNet, a hybrid segmentation framework that integrates CNN–Transformer synergy, multi-scale convolution, and boundary-aware decoding. Specifically, our design combines local–global feature fusion, large-kernel parallel convolutions without dilation, and a boundary refinement strategy to enhance lesion consistency. Extensive experiments on the ISIC 2016, ISIC 2017, and ISIC 2018 benchmarks demonstrate that IFGNet consistently surpasses state-of-the-art methods in Dice and IoU. These results highlight the effectiveness of IFGNet for accurate high-resolution skin lesion segmentation, and suggest its potential for clinical computer-aided melanoma diagnosis. Similar content being viewed by others FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Author informationAuthors and AffiliationsThe College of Electronic Information, Shanghai Dianji University, Shanghai, 201306, ChinaBo Li, Peiwen Tan, Jie Jia & Xinyan ChenAuthorsBo Li Peiwen TanJie JiaXinyan ChenCorresponding authorCorrespondence to Peiwen Tan.Ethics declarations Competing interests The authors declare no competing interests. Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link t...
Skin Cancer | Compendium | AJMC
The AJMC® Skin Cancer compendium is a comprehensive resource for clinical news and expert insights about melanoma and other skin cancers.
Perspective Chapter: Machine Learning Models in Early ... - IntechOpen
Skin cancer remains one of the most common malignancies worldwide, with early detection and personalised risk stratification being key to improving patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool in dermatology, offering enhanced diagnostic accuracy, risk prediction, and clinical decision-making as a support for clinicians and patients. This chapter ...
Melanoma Monday: Experts Warn Of Skin Cancer Symptoms And Urge Early ...
Deadliest skin cancer claims thousands yearly; doctors offer advice on red flags and prevention. Melanoma Monday serves as an important reminder for individuals to remain vigilant about changes in their skin and to recognize the signs of melanoma, the deadliest type of skin cancer. Each year, the United States sees an estimated 112,000 new cases of invasive melanoma diagnosed, with more than ...
