ArticleSensors (Basel, Switzerland)2023
Transformer-Based Approach to Melanoma Detection.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SkinFormer: a hybrid vision transformer and ConvNeXtV2 approach for skin cancer detection and segmentation.Scientific reports · 2026Article
- Hybrid vision transformer and graph neural network model with region-adaptive attention for enhanced skin cancer prediction.Scientific reports · 2026Article
- Explainable depth-wise and channel-wise fusion models for multi-class skin lesion classification.PloS one · 2026Article
- Automatic melanoma detection using an optimized five-stream convolutional neural network.Scientific reports · 2025Article
- Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.Journal of imaging informatics in medicine · 2025Article
- Review of Non-Invasive Imaging Technologies for Cutaneous Melanoma.Biosensors · 2025Review
- Fine-tuned deep transfer learning: an effective strategy for the accurate chronic kidney disease classification.PeerJ. Computer science · 2025Article
- The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948.
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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.