Skin cancer detection using the HAM10000 dataset: a comparative study of machine learning models
Abstract
Skin cancer is a prevalent and potentially life-threatening disease that requires early and accurate detection for effective treatment. Existing skin cancer detection procedures face limitations in terms of accuracy and efficiency. To overcome these limitations, we put forward a novel solution that leverages machine learning models to classify seven types of skin cancers using the HAM10000 dataset. This dataset consists of diverse clinical images, providing a valuable resource for developing robust classification models. Our approach involves preprocessing the HAM10000 dataset to ensure data consistency and enhance model performance. We apply various machine learning models, including KNN, Decision Tree, Random Forest, Ridge Classifier, and SVM, to classify the different types of skin cancers. Through rigorous evaluation, our models demonstrate promising results. Among the models tested, the Random Forest algorithm exhibits superior performance, achieving a validation set accuracy of 95.5% and a test set accuracy of 95.6%. These results highlight the effectiveness of our proposed methodology in accurately classifying skin cancers and surpassing the performance of traditional detection procedures. By utilizing machine learning techniques, our solution offers an improved approach to skin cancer detection, addressing the limitations of existing methods. The integration of Random Forest further enhances accuracy, enabling early and accurate diagnosis, which is crucial for successful treatment outcomes and improved patient care.
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Data augmentation is typically treated as supplementary rather than integral.
“Data augmentation is typically treated as supplementary rather than integral (11, 12).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [12]
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 [12]
Early studies relied on hand-crafted features, which were limited in capturing complex lesion patterns.
“Early studies relied on hand-crafted features (12), which were limited in capturing complex lesion patterns.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [12]
A limited number of works adopt SMOTE-based oversampling, which is more suitable for structured data than high-dimensional image representations.
“A limited number of works adopt SMOTE-based oversampling (12, 16), which is more suitable for structured data than high-dimensional image representations.”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [12]
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Sonuç, E., Saihood, Q., Yargı Baydilli, Y., & Özcan, E. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1871689
“Data augmentation is typically treated as supplementary rather than integral (11, 12).”
Influential
- Authors
- Velaga, N., Vardineni, V. R., Tupakula, P., & Pamidimukkala, J. S.
- Affiliations
- National Institute of Technology Raipur (IN)
- National Institute of Technology Jamshedpur (IN)
- SRM University, Andhra Pradesh (IN)
- Venue
- 2023 Global Conference on Information Technologies and Communications (GCITC)
- Publisher
- IEEE
- Published
- 2023-12-01
- Pages
- 1-7
- OpenAlex
- W4394911491
- Cited by
- 5
- References
- 25
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 3.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Global Conference on Information Technologies and Communications (GCITC) (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin cancer detection, skin lesion classification, HAM10000 dataset, random forest, support vector machine, k-nearest neighbors, decision tree, ridge classifier, dermatological image classification, early diagnosis
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