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A Novel Vision Transformer Model for Skin Cancer Classification
Open accessAbstract
Abstract Skin cancer can be fatal if it is found to be malignant. Modern diagnosis of skin cancer heavily relies on visual inspection through clinical screening, dermoscopy, or histopathological examinations. However, due to similarity among cancer types, it is usually challenging to identify the type of skin cancer, especially at its early stages. Deep learning techniques have been developed over the last few years and have achieved success in helping to improve the accuracy of diagnosis and classification. However, the latest deep learning algorithms still do not provide ideal classification accuracy. To further improve the performance of classification accuracy, this paper presents a novel method of classifying skin cancer in clinical skin images. The method consists of four blocks. First, class rebalancing is applied to the images of seven skin cancer types for better classification performance. Second, an image is preprocessed by being split into patches of the same size and then flattened into a series of tokens. Third, a transformer encoder is used to process the flattened patches. The transformer encoder consists of N identical layers with each layer containing two sublayers. Sublayer one is a multihead self-attention unit, and sublayer two is a fully connected feed-forward network unit. For each of the two sublayers, a normalization operation is applied to its input, and a residual connection of its input and its output is calculated. Finally, a classification block is implemented after the transformer encoder. The block consists of a flattened layer and a dense layer with batch normalization. Transfer learning is implemented to build the whole network, where the ImageNet dataset is used to pretrain the network and the HAM10000 dataset is used to fine-tune the network. Experiments have shown that the method has achieved a classification accuracy of 94.1%, outperforming the current state-of-the-art model IRv2 with soft attention on the same training and testing datasets. On the Edinburgh DERMOFIT dataset also, the method has better performance compared with baseline models.
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“Most prior studies address this issue through conventional image-level augmentation (7, 14, 15, 17, 18, 20, 21, 23), which increases data volume but does not introduce genuine semantic diversity.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [7]
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“In the classification stage, most prior studies relied primarily on the fully connected layer for final classification (7, 14, 16–18, 20).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [7]
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“Only limited works provided visual interpretation using attention maps (7) or Grad-CAM (13, 23, 28).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [7]
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“Class imbalance remains a central challenge in seven-class skin lesion classification using HAM10000.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [7]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762
Siegel, R., Naishadham, D., & Jemal, A. (2012) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20138 · Cited by 10,679
Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628
Gaussian Error Linear Units (GELUs)
Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484
Research works printed but not linked: 6
- Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA 68(6):394–424
- Siegel RL, Miller KD, Jemal A (2019) Cancer statistics. CA 69(1):7–3
- Vestergaard ME, Macaskill PH, Holt PE, Menzies SW (2008) Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br J Dermatol 159(3):669–676
- Datta SK, Shaikh MA, Srihari SN (2021) Soft Attention Improves Skin Cancer Classification Performance. InInterpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data. Springer, Cham, pp 13–23
- Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI blog 1(8):9
- Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Steiner A, Keysers D, Uszkoreit J, Lucic M (2021) Mlp-mixer: an all-mlp architecture for vision. Adv Neural Inf Process Syst 6(34):24261–24272
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Items 101–129 of 129
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Güneri, G., Yazar, E. K., Yazar, M. F., Çorbacı, K., Yıldırım, M. S., & Dandıl, E. (2026) · Diagnostics · doi:10.3390/diagnostics16081152
Bhargavi, M., & Balakrishna, S. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.04.020 · Cited by 1
Rahman, S., udddin, N., Ahmad, H. I., Ali, M., Fazal, S., Ahmad, A., Sultan, N., & Mashal (2026) · ICT Express · doi:10.1016/j.icte.2026.04.010
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Pavani, T., Kosaraju, S., Hemalatha, M., Dolly, D. R. J., Krishna, D. M., & Yadav, Y. C. (2026) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit69683.2026.11620693
Karantina, E., Kantaros, A., Nikolaou, G., Laskaris, N., & Zacharia, P. T. (2026) · Algorithms · doi:10.3390/a19080638
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Mittal, U., Varpe, S., Sharma, A. S., Sood, T., Khandnor, P., Garg, S., Rani, S., Gupta, M., Amraoui, L. E., Ouni, K., et al. (2026) · IEEE Access · doi:10.1109/access.2026.3719871
Goyal, P. K., Bibhu, V., & Khatter, H. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23317-2_44
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Singh, S., Vaishya, A., & Yadav, H. (2026) · International Conference on Sustainable Engineering and Technology Innovations (ICSETI) · doi:10.1109/icseti67678.2026.11636501
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Afzal, M., Shah, J. H., Saleem, R., Younus, M. U., Tahir, A., & Saeed, M. E. S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1885137
Vijayakumar Reddy, D., & Balaji, L. (2026) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit69151.2026.11656516
Dillshad, V., & Aldukhail, M. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102674
Gupta, P., Tyagi, H., & Gupta, N. (2026) · Research Square · doi:10.21203/rs.3.rs-10799468/v1
Meng, Y., & Li, B. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.08.172
Advances in Automated Skin Cancer Detection Using Deep Learning Methods
Kaushik, S., & Dahiya, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30957-0_34
A Novel CBAM-Integrated EfficientNet-B5 with FPN for Improved Skin Lesion Classification
Vasanth, T., Priyadarsini, M. J. P., & Nisha, J. S. (2026) · International Conference on Innovations in Mechanical, Robotics, Computing and Biomedical Engineering (ICIMRBE) · doi:10.1109/icimrbe70745.2026.11710086
SkinFusionNet: Multi-backbone spatial fusion for robust and explainable melanoma classification
Ahmmed, F., Rafsan, R. S., Mansib, M., Akther, A., Esika, A. E., & Mohaimen-Bin-Noor (2026) · Photodiagnosis and Photodynamic Therapy · doi:10.1016/j.pdpdt.2026.105668
Mushtaq, S., & Singh, O. (2026) · SN Computer Science · doi:10.1007/s42979-026-05385-8
SkinSense: Dermatological Screening and Skin-Care Advisory System
Srivarshini, S., Sofia, V. S. A., & Selvam, N. (2026) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2612557
Saihood, Q., Sonuç, E., & Baydi̇lli̇, Y. Y. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70459
Sharma, R., Kumar, S., Abdulla, S., Bajaj, K., Prabha, C., & Jain, S. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200746
A Hybrid Swin Transformer–ResNet50 Architecture for Skin Lesion Classification
Bulduk, H., & Sabancı, K. (2026) · Balkan Journal of Electrical and Computer Engineering · doi:10.17694/bajece.1925042
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Metadata
- Authors
- Yang, G., Luo, S., & Greer, P.
- Affiliations
- University of Newcastle Australia (AU)
- Corresponding author
- Yang, Guang
- Venue
- Neural Processing Letters
- Published
- 2023-03-27
- Volume
- 55
- Issue
- 7
- Pages
- 9335-9351
- ISSN
- 1370-4621 (ISSN-L)
- 1573-773X
- OpenAlex
- W4360994757
- Cited by
- 129
- References
- 31
- Citations by year
- 2026: 48 · 2025: 43 · 2024: 32 · 2023: 6
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Neural Processing Letters (publishedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion, cc-by)
- Funders
- The University of Newcastle
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin cancer classification, vision transformer, Transformer encoder, multi-head self-attention, class rebalancing, dermoscopic images, transfer learning, HAM10000 dataset
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