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Cited by 144Cutaneous Melanoma Detection and ManagementAI in cancer detectionArtificial Intelligence in Healthcare

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 access
Melarkode, Navneet, Srinivasan, Kathiravan, Qaisar, Saeed Mian, Plawiak, Pawel·2023·Cancers
DOI 10.3390/cancers15041183PMC PMC9953963
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Abstract

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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Registry

Crossref · 10.3390/cancers15041183

Cites

129 of 129 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 Flow diagram for the selection process of research articles using PRISMA method.
Figure 1 Flow diagram for the selection process of research articles using PRISMA method.CC BY · PubMed Central
Figure 2 Number of papers per year, used in the review.
Figure 2 Number of papers per year, used in the review.CC BY · PubMed Central
Figure 3 ML and DL methods versus frequency of papers used in this work.
Figure 3 ML and DL methods versus frequency of papers used in this work.CC BY · PubMed Central
Figure 4 Structure of this review.
Figure 4 Structure of this review.CC BY · PubMed Central
Figure 5 Dermoscopic sample images of skin cancer: (a) squamous cell carcinoma, (b) basal cell carcinoma, (c) benign dermatofibroma, (d) benign seborrheic keratosis, (e) benign actinic keratosis, and (f) malignant melanoma.
Figure 5 Dermoscopic sample images of skin cancer: (a) squamous cell carcinoma, (b) basal cell carcinoma, (c) benign dermatofibroma, (d) benign seborrheic keratosis, (e) benign actinic keratosis, and (f) malignant melanoma.CC BY · PubMed Central
Figure 6 Clinical sample images of skin cancer: (a) malignant melanoma, (b) squamous cell carcinoma, and (c) basal cell carcinoma.
Figure 6 Clinical sample images of skin cancer: (a) malignant melanoma, (b) squamous cell carcinoma, and (c) basal cell carcinoma.CC BY · PubMed Central
Figure 7 Current machine learning models in skin cancer diagnosis: tree illustration.
Figure 7 Current machine learning models in skin cancer diagnosis: tree illustration.CC BY · PubMed Central
Figure 8 Current deep learning models for skin cancer diagnosis: tree illustration.
Figure 8 Current deep learning models for skin cancer diagnosis: tree illustration.CC BY · PubMed Central
Figure 9 Open challenges in skin cancer diagnosis.
Figure 9 Open challenges in skin cancer diagnosis.CC BY · PubMed Central
Figure 10 Future research directions of skin cancer diagnosis.
Figure 10 Future research directions of skin cancer diagnosis.CC BY · PubMed Central

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Citations

Cites

2 retracted

Show all 131 references

Research works printed but not linked: 6

  1. Nahata, H., and Singh, S.P. (2020). Machine Learning with Health Care Perspective, Springer.
  2. Alwan, 2022, Skin cancer images classification using naïve bayes, Emergent J. Educ. Discov. Lifelong Learn., 3, 19
  3. Anas, 2017, Skin cancer classification using K-means clustering, Int. J. Tech. Res. Appl., 5, 62
  4. Peter Soosai Anandaraj, A., Gomathy, V., Amali Angel Punitha, A., Abitha Kumari, D., Sheeba Rani, S., and Sureshkumar, S. (2021). Cognitive Internet of Medical Things for Smart Healthcare, Springer.
  5. Gerke, S., Minssen, T., and Cohen, G. (2020). Artificial Intelligence in Healthcare, Academic Press.
  6. Barata, C., Marques, J.S., and Celebi, M.E. (2014). 2014 IEEE International Conference on Image Processing (ICIP), IEEE.

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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
ORCID
Venue
Cancers
Publisher
MDPI AG
Published
2023-02-13
Volume
15
Issue
4
Pages
1183
ISSN
2072-6694 (ISSN-L)
DOI
10.3390/cancers15041183
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
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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