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.
“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]
Only limited works provided visual interpretation using attention maps or Grad-CAM.
“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 [23]
The performance of these models remained moderate compared to more advanced transformer or hybrid architectures.
“However, the performance of these models remained moderate compared to more advanced transformer (23) or hybrid architectures (16).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [23]
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- 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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