Boosting skin cancer diagnosis accuracy with ensemble approach
Open accessAbstract
Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced. It's still difficult to analyze the images to differentiate benign from malignant tumors, though. Better predictive modeling methods are needed since the diagnostic procedures used now frequently produce inaccurate and inconsistent results. In dermatology, Machine learning (ML) models are becoming essential for the automatic detection and classification of skin cancer lesions from image data. With the ensemble model, which mix several ML approaches to take use of their advantages and lessen their disadvantages, this work seeks to improve skin cancer predictions. We introduce a new method, the Max Voting method, for optimization of skin cancer classification. On the HAM10000 and ISIC 2018 datasets, we trained and assessed three distinct ML models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), and Support Vector Machine (SVM). Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. We demonstrate that the Max Voting method greatly improves predictive performance, reaching an accuracy of 94.70% and producing the best results for F1-measure, recall, and precision. The most dependable and robust approach turned out to be Max Voting, which combines the benefits of numerous pre-trained ML models to provide a new and efficient method for classifying skin cancer lesions.
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Most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions.
“However, most prior studies primarily focused on improving classification accuracy through increasingly complex architectures without sufficient attention to explaining model decisions (14–22).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [22]
The literature review and the comparison of state-of-the-art models describe prior skin-cancer classifiers using CNNs, hybrid models, and ensembles to improve accuracy. They do not say that most prior studies used increasingly complex architectures or gave insufficient attention to explaining model decisions; that part of the claim is unsupported by our copy. (LLM, conf=0.83)
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Shafiq, Y., & Shahzad, H. M. (2026) · Journal of Innovative Research and Technology · doi:10.66857/131a
Akinbo, R. S., Adeyemi, T., & Kuboye, B. M. (2026) · Scientific Journal of Computer Science · doi:10.64539/sjcs.v2i2.2026.416
Speech Emotion Classification Using MFCC Feature Extraction and Bagging-Based Ensemble Learning
Haristyawan, I., Arriyanti, E., & Wahyuni, W. (2025) · Building of Informatics Technology and Science (BITS) · doi:10.47065/bits.v7i3.8878
Khan, M. F., & Ahmad, K. (2026) · Diagnostics · doi:10.3390/diagnostics16152453
Chinthaginjala, R., Tera, S. P., El-Shafai, W., Azar, A. T., & Kamal, N. A. (2026) · International Conference on Control, Automation and Diagnosis (ICCAD) · doi:10.1109/iccad69956.2026.11642994
Self-citationkhan, H., Agha, D., Mastoi, Q.-U.-A., Mehreen, S., & Shahriar, A. (2026) · Journal of Artificial Intelligence in Medical Applications · doi:10.65511/jaima.v2i1.1173
A Framework for Skin Cancer Classification using Ensemble Transfer Learning and Data Fusion
Aftab, S., Mubarik, T., & Saeed, M. A. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1991
Multi-Class Skin Cancer Classification: Using ConvNeXt as A Feature Extractor in A Stacking Scheme
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Enhanced ConvNeXt model for dermoscopic skin lesion classification with cross-domain generalization
Habeeb, Z. Q., Vuksanović, B., & Al-Zaydi, I. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21927-x
Explainable AI framework for skin lesion imaging based cancer detection
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- Authors
- Natha, P., Tera, S. P., Chinthaginjala, R., Rab, S. O., Narasimhulu, C. V., & Kim, T. H.
- Affiliations
- Koneru Lakshmaiah Education Foundation (IN)
- Indian Institute of Technology Guwahati (IN)
- Vellore Institute of Technology University (IN)
- King Khalid University (SA)
- Chaitanya Bharathi Institute of Technology (IN)
- Zhejiang University of Science and Technology (CN)
- Corresponding author
- Chinthaginjala, Ravikumar
- Kim, Tae Hoon
- Venue
- Scientific Reports
- Published
- 2025-01-08
- Volume
- 15
- Issue
- 1
- Pages
- 1290-1290
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11711234
- OpenAlex
- W4406159072
- Cited by
- 45
- References
- 63
- Citations by year
- 2026: 22 · 2025: 23
- FWCI
- 18.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Khalid University
- Natural Science Foundation of Zhejiang Province (LQ23F010004)
- 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 classification, ensemble learning, dermoscopic images, HAM10000, ISIC 2018, genetic algorithm feature selection, random forest, support vector machine, multilayer perceptron, benign versus malignant differentiation
- MeSH terms
- Machine Learning, Algorithms, Humans, Skin Neoplasms, Neural Networks, Computer, Dermoscopy, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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