The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer
Open accessAbstract
Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases. However, there are certain challenges associated with the accurate classification of these diseases such as a lower detection accuracy, poor generalization of the models, and an insufficient amount of labeled data for training. To address these challenges, in this work we developed a two-tier framework for the accurate classification of SC. During the first stage of the framework, we applied different methods for data augmentation to increase the number of image samples for effective training. As part of the second tier of the framework, taking into consideration the promising performance of the Medical Vision Transformer (MVT) in the analysis of medical images, we developed an MVT-based classification model for SC. This MVT splits the input image into image patches and then feeds these patches to the transformer in a sequence structure, like word embedding. Finally, Multi-Layer Perceptron (MLP) is used to classify the input image into the corresponding class. Based on the experimental results achieved on the Human Against Machine (HAM10000) datasets, we concluded that the proposed MVT-based model achieves better results than current state-of-the-art techniques for SC classification.
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“A smaller number of works employed ML classifiers (12, 23).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [23]
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“Only limited works provided visual interpretation using attention maps (7) or Grad-CAM (13, 23, 28).”
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2 retracted
[Retracted] Vision Sensor‐Based Real‐Time Fire Detection in Resource‐Constrained IoT Environments
Yar, H., Hussain, T., Khan, Z. A., Koundal, D., Lee, M. Y., & Baik, S. W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5195508 · Cited by 80
RetractedDeep learning-based election results prediction using Twitter activity
Ali, H., Farman, H., Yar, H., Khan, Z., Habib, S., & Ammar, A. (2021) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-021-06569-5 · Cited by 42
Self-citationRetractedTransMorph: Transformer for unsupervised medical image registration
Chen, J., Frey, E. C., He, Y., Segars, W. P., Li, Y., & Du, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102615 · Cited by 883
Attention Residual Learning for Skin Lesion Classification
Zhang, J., Xie, Y., Xia, Y., & Shen, C. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2893944 · Cited by 585
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020) · IEEE Access · doi:10.1109/access.2020.3009537 · Cited by 557
TransMed: Transformers Advance Multi-Modal Medical Image Classification
Dai, Y., Gao, Y., & Liu, F. (2021) · Diagnostics · doi:10.3390/diagnostics11081384 · Cited by 511
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Medical Image Segmentation Methods, Algorithms, and Applications
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A multi-class skin Cancer classification using deep convolutional neural networks
Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09388-2 · Cited by 297
Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.06.049 · Cited by 267
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- Yar, 2021, Fire Detection via Effective Vision Transformers, J. KINGComputing, 17, 21
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Alrahhal, M., Bonny, T., Hussain, A. J., Al-Shabi, M. A. Q., Hamad, M. S., & Yateem, N. A. (2025) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset66891.2025.11428148
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Zhou, F., Hu, S., Xiaozheng, D. U., Li, N., & Zhou, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-45033-y
Balde, A., Ossami, D.-D. O., Sall, O., Faye, Y., Bassene, A., Soumboundou, M., Sall, C., M’kacher, R., & Faty, L. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11467872
CDMFuse: A Multi-modal Fusion Framework for Skin Lesion Classification
Dong, X., Yu, L., Tian, S., Yang, Q., Zhang, D., & Yu, S. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7299-1_10
An Analytical Study of Vision Transformer Models for Medical Image Classification and Segmentation
Kassara, W. A. (2026) · Journal of Al-Turath University College · doi:10.63964/jatuc.43.1.2026.17
Tang, W., Zhong, H., Cao, Z., Wu, K., Xi, D., Zhang, X., Yang, P., Zhou, Y., & Ma, C. (2026) · Journal of Earth Science · doi:10.1007/s12583-025-0328-9
Skin Cancer Classification Using Vision Transformers Based on Practical Optimizers
Norouzi, A., Ebrahimnezhad, H., & Yeganehmehr, A. (2025) · International Conference on Signal Processing and Intelligent Systems (ICSPIS) · doi:10.1109/icspis68676.2025.11551747
Federated Cloud Learning Model for Privacy-Preserving Skin Cancer Detection Using Image Processing
Krishnamoorthy, P., Ravishankar, K., Auguskani, J. P. L., Donthala, K. K., Acharjee, P. B., & Barkunan, S. R. (2026) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit69683.2026.11621374
Advances in artificial intelligence paradigms in medical image based disease diagnosis
Arora, A., & Tiwari, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115812
Integrating DenseNet and Transformer Encoders for Advanced Skin Cancer Classification
Aburass, S., Aryal, A., Argue, S., & Dorgham, O. M. (2026) · International Conference on Artificial Intelligence, Robotics and Control (AIRC) · doi:10.1109/airc69745.2026.11631446
End-to-End Ground-Up Vision Transformer for Skin Disease Detection
Singh, S., Vaishya, A., & Yadav, H. (2026) · International Conference on Sustainable Engineering and Technology Innovations (ICSETI) · doi:10.1109/icseti67678.2026.11636501
Raza, A., Ali, A., Ullah, S., Rehman, B., & Ali, Z. (2026) · PLoS ONE · doi:10.1371/journal.pone.0355613
Vijayakumar Reddy, D., & Balaji, L. (2026) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit69151.2026.11656516
Tsai, M.-H., Veda, J., Leu, J., & Tsai, C. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02133-5
Dillshad, V., & Aldukhail, M. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102674
Meng, Y., & Li, B. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.08.172
Habeeb, R., Abiyev, R. H., & Dimililer, K. (2026) · Electronics · doi:10.3390/electronics15184308
Saranya, S., & Khanna, M. R. (2026) · International Conference on Natural Language Processing and Computer Vision (ICNPCV) · doi:10.1109/icnpcv69434.2026.11708834
Kathuria, P., & Nehra, D. (2026) · International Conference on Computer, Mechatronics, Robotics & Artificial Intelligence (ICCMRAI) · doi:10.1109/iccmrai67732.2026.11724138
A Hybrid Swin Transformer–ResNet50 Architecture for Skin Lesion Classification
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Metadata
- Authors
- Aladhadh, S., Alsanea, M., Aloraini, M., Khan, T., Habib, S., & Islam, M.
- Affiliations
- Qassim University (SA)
- Arab East Colleges (SA)
- Islamia College University (PK)
- Onaizah Colleges (SA)
- Corresponding author
- Habib, Shabana
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-05-25
- Volume
- 22
- Issue
- 11
- Pages
- 4008
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9182815
- OpenAlex
- W4281485012
- Cited by
- 113
- References
- 60
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 29 · 2023: 23 · 2022: 3
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research, Qassim University
- 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, skin lesion classification, data augmentation, HAM10000 dataset, multilayer perceptron, early detection of skin cancer
- MeSH terms
- Machine Learning, Humans, Skin Neoplasms, Neural Networks, Computer, Early Detection of Cancer
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