The DOI matches its Crossref record; 78 of the 81 references it cites were found in a registry or an index.
Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities
Open accessAbstract
Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.
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“A questionnaire was formed which evaluates the quality of selected papers [18].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [18]
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Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000100 · Cited by 28,164
Siegel, R. L., Miller, K. D., & Jemal, A. (2018) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21442 · Cited by 16,632
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00136 · Cited by 10,349
Systematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · cited 1×
“Machine learning implements statistical algorithms for learning purposes, which first train the data, then test that data, by adopting their parameters [11].”
BackgroundHaenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., et al. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy166 · Cited by 1,571
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Yu, L., Chen, H., Dou, Q., Qin, J., & Heng, P.-A. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2642839 · Cited by 1,140
PH2 - A dermoscopic image database for research and benchmarking
Mendonca, T., Ferreira, P. M., Marques, J. S., Marcal, A. R. S., & Rozeira, J. (2013) · 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610779 · Cited by 1,009
Research works printed but not linked: 16
- 2019, DermQuest Online Medical Resource | Home
- 2019, Interactive Dermatology Atlas | Home
- mendonca, 2013, PH2–A dermoscopic image database for research and benchmarking, Proc 35th Annu Int Conf IEEE Eng Med Biol Soc (EMBC), 5437
- 2019, The International Skin Imaging Collaboration | Challenges
- 2019, DermIS Dermatology Information System | Home
- 2019, DermQuest Online Medical Resource | Home
- 2020, NCBI | Advance Search
- 2019, American Skin Cancer Organization |Skin Cancer Information
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- 2020, International Skin Imaging Collaboration | ISIC 2019
- 2020, Wiley Online Library | Advance Search
- 2019, FWHO | Ultraviolet Radiation
- 2020, IEEE IEEE Xplore
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Cited by
Sorted by citations
Tahir, M., Naeem, A., Malik, H., Tanveer, J., Naqvi, R. A., & Lee, S.-W. (2023) · Cancers · doi:10.3390/cancers15072179 · Cited by 240
Adegun, A. A., & Viriri, S. (2020) · IEEE Access · doi:10.1109/access.2020.3016651 · Cited by 212
Classification of skin cancer from dermoscopic images using deep neural network architectures
Jaisakthi, S. M., Mirunalini, P., Aravindan, C., & Appavu, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13847-3 · Cited by 159
Ahamed, M. F., Hossain, M. M., Nahiduzzaman, M., Islam, M. R., Islam, M. R., Ahsan, M., & Haider, J. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102313 · Cited by 151
Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023) · Cancers · doi:10.3390/cancers15041183 · Cited by 144
Metadata
- Authors
- Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A.
- Affiliations
- University of Management and Technology (PK)
- Abu Dhabi University (AE)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 110575-110597
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3035741257
- Cited by
- 189
- References
- 81
- Citations by year
- 2026: 18 · 2025: 24 · 2024: 41 · 2023: 45 · 2022: 41 · 2021: 18 · 2020: 2
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- melanoma, melanoma classification, skin cancer detection, convolutional neural networks, binary classification, dermatological image analysis, classifier accuracy comparison
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