Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review
Open accessAbstract
Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.
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The Reflectance Confocal Microscopy technique can be combined with a CNN to advance non-invasive skin disease diagnosis.
“the Reflectance Confocal Microscopy technique can be combined with a CNN to advance non-invasive skin disease diagnosis [44,45].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [45]
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QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199 · see citing works
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
Disparities in dermatology AI performance on a diverse, curated clinical image set
Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al. (2022) · Science Advances · doi:10.1126/sciadv.abq6147 · Cited by 544 · see citing works
Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data
Gessert, N., Nielsen, M., Shaikh, M., Werner, R., & Schlaefer, A. (2020) · MethodsX · doi:10.1016/j.mex.2020.100864 · Cited by 399 · see citing works
Al‐masni, M. A., Kim, D., & Kim, T. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105351 · Cited by 385 · see citing works
What is AI? Applications of artificial intelligence to dermatology
Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18880 · Cited by 355 · see citing works
Iqbal, I., Younus, M., Walayat, K., Kakar, M. U., & Ma, J. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101843 · Cited by 343 · see citing works
Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks
Tschandl, P., Rosendahl, C. O., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J. F., Lallas, A., et al. (2018) · JAMA Dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 327 · see citing works
Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification
Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105475 · Cited by 320 · see citing works
A GAN-based image synthesis method for skin lesion classification
Qin, Z., Liu, Z., Zhu, P., & Xue, Y. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105568 · Cited by 300 · see citing works
Pacheco, A. G. C., & Krohling, R. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3062002 · Cited by 268 · see citing works
An improved transformer network for skin cancer classification
Xin, C., Liu, Z., Zhao, K., Miao, L., Ma, Y., Zhu, X., Zhou, Q., Wang, S., Li, L., Yang, F., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105939 · Cited by 252 · see citing works
Skin Lesion Classification Using CNNs With Patch-Based Attention and Diagnosis-Guided Loss Weighting
Gessert, N., Sentker, T., Madesta, F., Schmitz, R., Kniep, H. C., Baltruschat, I. M., Werner, R., & Schlaefer, A. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2915839 · Cited by 185 · see citing works
Machine learning and deep learning algorithms for skin cancer classification from dermoscopic images
Bechelli, S., & Delhommelle, J. (2022) · Bioengineering · doi:10.3390/bioengineering9030097 · Cited by 181 · see citing works
Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet
Hosny, K. M., Kassem, M. A. A., & Fouad, M. M. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00371-9 · Cited by 178 · see citing works
Machine Learning and Its Application in Skin Cancer
Das, K., Cockerell, C. J., Patil, A. D., Pietkiewicz, P., Giulini, M., Grabbe, S., & Goldust, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413409 · Cited by 163 · see citing works
InSiNet: a deep convolutional approach to skin cancer detection and segmentation
Reis, H. Ç., Turk, V., Khoshelham, K., & Kaya, S. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-021-02473-0 · Cited by 161 · see citing works
Afza, F., Sharif, M. I., Khan, M. A., Tariq, U., Yong, H.-S., & Cha, J. (2022) · Sensors · doi:10.3390/s22030799 · Cited by 159 · see citing works
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 · see citing works
Multi-features extraction based on deep learning for skin lesion classification
Benyahia, S., Meftah, B., & Lézoray, O. (2021) · Tissue and Cell · doi:10.1016/j.tice.2021.101701 · Cited by 144 · see citing works
Combalia, M., Codella, N., Rotemberg, V. M., Carrera, C., Dusza, S. W., Gutman, D. A., Helba, B., Kittler, H., Kurtansky, N. R., Liopyris, K., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00021-8 · Cited by 144 · see citing works
FusionM4Net: a multi-stage multi-modal learning algorithm for multi-label skin lesion classification
Tang, P., Yan, X., Nan, Y., Xiang, S., Krammer, S., & Lasser, T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102307 · Cited by 131 · see citing works
Naeem, A., Anees, T., Fiza, M., Naqvi, R. A., & Lee, S. W. (2022) · Sensors · doi:10.3390/s22155652 · Cited by 128 · see citing works
Dascalu, A., & David, E. O. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.04.055 · Cited by 126 · see citing works
A hierarchical three-step superpixels and deep learning framework for skin lesion classification
Afza, F., Sharif, M., Mittal, M., Khan, M. A., & Hemanth, D. J. (2021) · Methods · doi:10.1016/j.ymeth.2021.02.013 · Cited by 118 · see citing works
Semi-supervised learning for medical image classification using imbalanced training data
Huynh, T., Nibali, A., & He, Z. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106628 · Cited by 115 · see citing works
A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images
Ameri, A. (2020) · Journal of Biomedical Physics and Engineering · doi:10.31661/jbpe.v0i0.2004-1107 · Cited by 112 · see citing works
New Trends in Melanoma Detection Using Neural Networks: A Systematic Review
Popescu, D., El-Khatib, M., El-Khatib, H., & Ichim, L. (2022) · Sensors · doi:10.3390/s22020496 · Cited by 94 · see citing works
Patel, R. H., Foltz, E. A., Witkowski, A. M., & Łudzik, J. (2023) · Cancers · doi:10.3390/cancers15194694 · Cited by 88 · see citing works
Self-citationRefined Residual Deep Convolutional Network for Skin Lesion Classification
Hosny, K. M., & Kassem, M. A. A. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00552-0 · Cited by 84 · see citing works
Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks
Popescu, D., El-Khatib, M., & Ichim, L. (2022) · Sensors · doi:10.3390/s22124399 · Cited by 83 · see citing works
Qian, S., Ren, K., Zhang, W., & Ning, H. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107166 · Cited by 75 · see citing works
Artificial Intelligence for Skin Cancer Detection: Scoping Review
Takiddin, A., Schneider, J., Yang, Y. D., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/22934 · Cited by 72 · see citing works
Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer
Zhang, L., Gao, H. J., Zhang, J., & Badami, B. (2019) · Open Medicine · doi:10.1515/med-2020-0006 · Cited by 68 · see citing works
Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy
Campanella, G. S. V., Navarrete‐Dechent, C., Liopyris, K., Monnier, J., Aleissa, S., Minhas, B. K., Scope, A., Longo, C., Guitera, P., Pellacani, G., et al. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2021.06.015 · Cited by 68 · see citing works
Sangers, T. E., Reeder, S. W. I., van der Vet, S., Jhingoer, S., Mooyaart, A. L., Siegel, D. M., Nijsten, T. E. C., & Wakkee, M. (2022) · Dermatology · doi:10.1159/000520474 · Cited by 61 · see citing works
Huang, K., Jiang, Z., Li, Y., Wu, Z., Wu, X., Zhu, W., Chen, M., Zhang, Y., Zuo, K., Li, Y., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/26025 · Cited by 54 · see citing works
Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention
Nguyen, V. D., Bui, N. D., & Do, H. K. (2022) · Italian National Conference on Sensors · doi:10.3390/s22197530 · Cited by 54 · see citing works
Rezk, E., Eltorki, M., & El-Dakhakhni, W. W. (2022) · JMIR Research Protocols · doi:10.2196/34896 · Cited by 43 · see citing works
Lee, J. R. H., Pavlova, M., Famouri, M., & Wong, A. M. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00871-w · Cited by 41 · see citing works
Salvia, M. L., Torti, E., León, R., Fabelo, H. A., Ortega, S., Martinez-Vega, B., Callicó, G. M., & Leporati, F. (2022) · Sensors · doi:10.3390/s22166145 · Cited by 34 · see citing works
Dascalu, A., Walker, B., Oron, Y., et al. (2021) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-021-03809-x · Cited by 32 · see citing works
Razzak, I., & Naz, S. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3039358 · Cited by 31 · see citing works
Wang, S., Zhang, X.-Y., Liu, J., Tao, C., Zhu, C., Shu, C., Xu, T., & Jin, H. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001023 · Cited by 30 · see citing works
Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy
Wodziński, M., Skalski, A., Witkowski, A. M., Pellacani, G., & Łudzik, J. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856731 · Cited by 27 · see citing works
Self-citationWinkler, J. K., Sies, K., Fink, C., Toberer, F., Enk, A., Abassi, M. S., Fuchs, T., Blum, A., Stolz, W., Coras‐Stepanek, B., et al. (2021) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.14510 · Cited by 26 · see citing works
Li, C., Fei, W., Shen, C., Wang, Z., Jing, Y., Meng, R., & Cui, Y. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001002 · Cited by 25 · see citing works
Ho, C., Calderon‐Delgado, M., Chan, C., Lin, M., Tjiu, J., Huang, S.-L. L., & Chen, H. H. (2020) · Journal of Biophotonics · doi:10.1002/jbio.202000271 · Cited by 25 · see citing works
Minagawa, A., Koga, H., Sano, T., et al. (2020) · The Journal of Dermatology · doi:10.1111/1346-8138.15640 · Cited by 25 · see citing works
Skin Diseases Classification Using Hybrid AI Based Localization Approach
Sreekala, K., Rajkumar, N., Sugumar, R., et al. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6138490 · Cited by 24 · see citing works
Laverde‐Saad, A., Jfri, A. H., García, R., Fernández, I. S., Martínez, C., Cembrero, H., Roustan-Gullón, G., & Alfageme, F. (2021) · Skin Research and Technology · doi:10.1111/srt.13086 · Cited by 22 · see citing works
Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images
Salvia, M. L., Torti, E., León, R., Fabelo, H. A., Ortega, S., Balea-Fernandez, F., Martinez-Vega, B., Castaño, I., Almeida, P., Carretero, G., et al. (2022) · Sensors · doi:10.3390/s22197139 · Cited by 17 · see citing works
Chen, M., Feng, X., Fox, M. C., et al. (2022) · Journal of Biomedical Optics · doi:10.1117/1.jbo.27.6.065004 · Cited by 16 · see citing works
Salbiah, S. S., Arof, H., Harun, S. W., Wahab, A. W. A., & Idris, M. Y. I. (2022) · PLoS ONE · doi:10.1371/journal.pone.0274896 · Cited by 12 · see citing works
Dermoscopy lesion classification based on GANs and a fuzzy rank-based ensemble of CNN models
Li, H., Li, W., Chang, J., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac8b60 · Cited by 8 · see citing works
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Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“When focusing on microscopic images, the Reflectance Confocal Microscopy technique can be combined with a CNN to advance non-invasive skin disease diagnosis [44,45].”
- Authors
- Foltz, E. A., Witkowski, A., Becker, A. L., Latour, E., Lim, J. Y., Hamilton, A., & Ludzik, J.
- Affiliations
- Oregon Health & Science University (US)
- University of Hawaiʻi at Mānoa (US)
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97201, USA
- Corresponding author
- Foltz, Emilie A.
- ORCID
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2024-02-01
- Volume
- 16
- Issue
- 3
- Pages
- 629
- ISSN
- 2072-6694 (ISSN-L)
- PMC
- PMC10854803
- OpenAlex
- W4391432064
- Cited by
- 28
- References
- 55
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 6 · 2023: 2
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cancers (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Epidemiology › Nonmelanoma Skin Cancer Studies
- Keywords
- artificial intelligence, non-melanoma skin cancer, basal cell carcinoma, squamous cell carcinoma, dermoscopy, reflectance confocal microscopy, optical coherence tomography, non-invasive imaging, early detection, diagnostic accuracy, machine learning classification
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