Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images
Open accessAbstract
We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database.
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Most studies focus on binary classification rather than clinically relevant multiclass categorization.
“Most studies focus on binary classification rather than clinically relevant multiclass categorization (9, 10).”
From Interpretable multimodal fusion for skin lesion classification using dermoscopic images and patient metadata (2026) · cites this paper as [9]
The Conclusions and Results (pp. not available) show that this paper studies binary malignant-versus-benign classification. They do not establish that most studies use binary classification or compare its clinical relevance with multiclass categorization. (LLM, conf=0.82)
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- Authors
- Bechelli, S., & Delhommelle, J.
- Affiliations
- University of North Dakota (US)
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND 58202, USA
- ORCID
- Delhommelle, Jerome
- Venue
- Bioengineering
- Publisher
- MDPI AG
- Published
- 2022-02-27
- Volume
- 9
- Issue
- 3
- Pages
- 97
- ISSN
- 2306-5354 (ISSN-L)
- PMC
- PMC8945332
- OpenAlex
- W4214575762
- Cited by
- 181
- References
- 60
- Citations by year
- 2026: 27 · 2025: 61 · 2024: 49 · 2023: 37 · 2022: 7
- FWCI
- 16.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioengineering (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (OIA-1946202)
- 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, dermoscopic images, convolutional neural networks, transfer learning, VGG16, Xception, ResNet50, ensemble learning, class imbalance, machine learning classifier comparison
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