The DOI matches its Crossref record; 129 of the 131 references it cites were found in a registry or an index.
AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions
Open accessAbstract
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
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“Artificial intelligence (AI) for medical diagnosis holds potential, demonstrating high performance in detecting various diseases.57”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [5]
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2 retracted
RETRACTED ARTICLE: Skin lesion classification using decision trees and random forest algorithms
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Sun, J., & Huang, Y. R. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103769 · Cited by 3
RetractedDermatologist-level classification of skin cancer with deep neural networks
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Overview of Next‐Generation Sequencing Technologies
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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM
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Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review
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Özdemir, B., & Paçal, İ. (2025) · Scientific Reports · doi:10.1038/s41598-025-89230-7 · Cited by 153
Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
Sufyan, M., Shokat, Z., & Ashfaq, U. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107356 · Cited by 170
A novel CNN-ViT-based deep learning model for early skin cancer diagnosis
Paçal, İ., Ozdemir, B., Zeynalov, J., Gasimov, H., & Pacal, N. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107627 · Cited by 160
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A., Ouamane, A., & Mansoor, W. (2023) · Systems · doi:10.3390/systems11100519 · Cited by 125
AI'S IMPACT ON PERSONALIZED MEDICINE: TAILORING TREATMENTS FOR IMPROVED HEALTH OUTCOMES
Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024) · Engineering Science & Technology Journal · doi:10.51594/estj.v5i4.1040 · Cited by 74
Metadata
- Authors
- Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P.
- Affiliations
- Vellore Institute of Technology University (IN)
- Effat University (SA)
- Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
- Corresponding author
- Srinivasan, Kathiravan
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2023-02-13
- Volume
- 15
- Issue
- 4
- Pages
- 1183
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC9953963
- OpenAlex
- W4320492826
- Cited by
- 144
- References
- 131
- Citations by year
- 2026: 26 · 2025: 62 · 2024: 46 · 2023: 10
- FWCI
- 4.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- VIT University
- Politechnika Krakowska
- Effat University
- 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, automated diagnosis, early detection, machine learning, deep learning, dermatological image analysis, skin lesion classification, medical image dataset, access to specialist care
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