ReviewFrontiers in oncology2022
Skin Cancer Classification With Deep Learning: A Systematic Review.
Review in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification.Acta obstetricia et gynecologica Scandinavica · 2025Pooled it
- MorphoNet: An Interpretable Hierarchical Deep Learning Framework for Multi-Class Skin Lesion Classification Using Dermoscopic Morphology.Bioengineering (Basel, Switzerland) · 2026Article
- Causal Uncertainty-Decomposed Ensemble Learning for Confounding-Aware Skin Cancer Detection in Dermoscopic Images.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Skin Lesion and Cancer Detection Using Computer Vision: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2026Review
- Image-Driven Multimodal Deep Learning for Skin Cancer Diagnosis: Cross-Attention Fusion of Dermoscopic Imaging Clinical Data and Knowledge Graphs.Sensors (Basel, Switzerland) · 2026Article
- Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.The Journal of clinical endocrinology and metabolism · 2026Observational
- FairDITA: Disentangled Image-Text Alignment for Fair Skin Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Text guided cross attentive multimodal learning with visual feature modulation for automated skin lesion detection.Scientific reports · 2026Article
- DermaScanAI an explainable hybrid deep learning framework for automated skin lesion classification using dual attention and metadata fusion.Scientific reports · 2026Article
- How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review.Research square · 2026Article
- Advanced hybrid transformer CNN framework for improved skin lesion classification and segmentation.Scientific reports · 2026Article
- Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection.Scientific reports · 2026Article
- DL-PCMNet: Distributed Learning Enabled Parallel Convolutional Memory Network for Skin Cancer Classification with Dermatoscopic Images.Diagnostics (Basel, Switzerland) · 2026Article
- A novel multi-agent spatiotemporal fusion framework for intelligent skin cancer diagnosis.Frontiers in oncology · 2026Article
- SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering.Frontiers in neuroscience · 2026Article
- A hybrid deep learning and cellular automata framework with fractional derivatives for skin type and skin disease classification.Frontiers in digital health · 2026Article
- Lipid-based nanocarriers in combination chemotherapy: a promising strategy for advanced skin cancer management.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Effective skin cancer classification by modified and optimized inception-ResNet-V2 model.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer is one of the most dangerous diseases in the world. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. However, achieving automatic skin cancer classification is difficult because the majority of skin disease images used for training are imbalanced and in short supply; meanwhile, the model's cross-domain adaptability and robustness are also critical challenges. Recently, many deep learning-based methods have been widely used in skin cancer classification to solve the above issues and achieve satisfactory results. Nonetheless, reviews that include the abovementioned frontier problems in skin cancer classification are still scarce. Therefore, in this article, we provide a comprehensive overview of the latest deep learning-based algorithms for skin cancer classification. We begin with an overview of three types of dermatological images, followed by a list of publicly available datasets relating to skin cancers. After that, we review the successful applications of typical convolutional neural networks for skin cancer classification. As a highlight of this paper, we next summarize several frontier problems, including data imbalance, data limitation, domain adaptation, model robustness, and model efficiency, followed by corresponding solutions in the skin cancer classification task. Finally, by summarizing different deep learning-based methods to solve the frontier challenges in skin cancer classification, we can conclude that the general development direction of these approaches is structured, lightweight, and multimodal. Besides, for readers' convenience, we have summarized our findings in figures and tables. Considering the growing popularity of deep learning, there are still many issues to overcome as well as chances to pursue in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.