Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01037
Number of page(s) 12
DOI https://doi.org/10.1051/epjconf/202534101037
Published online 20 November 2025
  1. World Health Organization, Skin diseases as a public health priority: Resolution WHA78.6 (WHO, Geneva, Switzerland, 2025). [Online]. Available: https://apps.who.int/gb/ebwha/pdf_files/WHA78/A78_R6-en.pdf [Google Scholar]
  2. M. Sharma, A.N.J. Raj, E. Pardede, Indian skin tone datasets for computer vision and medical diagnosis: A critical need and evaluation. IEEE Access 11, 5698457000 (2023. https://doi.org/10.1109/ACCESS.2023.3291420 [Google Scholar]
  3. S.S. Han, I.J. Moon, W. Lim, et al., Keratinocytic skin cancer detection on the face using region-based convolutional neural network. J. Am. Acad. Dermatol. 82(6, 1527-1533 (2020. https://doi.org/10.1016/jjaad.2019.09.036 [Google Scholar]
  4. T.J. Brinker, A. Hekler, A.H. Enk, et al., Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur. J. Cancer 113, 47-54 (2020. https://doi.org/10.1016/j.ejca.2019.04.001 [Google Scholar]
  5. A. Mirzaei, et al., Skin type diversity in skin lesion datasets: A review. Curr. Dermatol. Rep. 108, 400-440 (2024. https://doi.org/10.1007/s13671-024-00440-0 [Google Scholar]
  6. G.A. Tadesse, et al., Skin tone analysis for representation in dermatology datasets. npj Digit. Med. (2023. https://doi.org/10.1038/s41746-023-00791-y [Google Scholar]
  7. Y. Liu, A. Jain, C. Eng, et al., A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26(6, 900-908 (2020. https://doi.org/10.1038/s41591-020-0842-3 [Google Scholar]
  8. A. Esteva, et al., Deep learning-enabled medical computer vision. Nat. Med. 27(11, 1663-1675 (2021. https://doi.org/10.1038/s41591-021-01644-9 [Google Scholar]
  9. M.A. Kassem, K.M. Hosny, R. Damasevicius, M. Shams, Machine learning and deep learning methods for skin lesion classification and diagnosis: A review. Diagnostics 11(8, 1365 (2021. https://doi.org/10.3390/diagnostics11081365 [Google Scholar]
  10. T.E. Sangers, et al., Towards successful implementation of AI in skin cancer care: Dermatologists' and GPs' views. Arch. Dermatol. Res. (2023. https://doi.org/10.1007/s00403-023-02621-0 [Google Scholar]
  11. B.H.M. Van der Velden, H.J. Kuijf, K.G.A. Gilhuijs, M.A. Viergever, Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med. Image Anal. 79, 102470 (2022. https://doi.org/10.1016Zj.media.2022.102470 [Google Scholar]
  12. I. Gonzalez-Diaz, A. Picon, Multimodal explainable AI for clinical skin lesion diagnosis. Med. Image Anal. 84, 102676 (2023. https://doi.org/10.1016/j.media.2023.102676 [Google Scholar]
  13. P. Probst, M.N. Wright, A.L. Boulesteix, Hyperparameters and tuning strategies for random forest. WIREs Data Min. Knowl. Discov. 10(3, e1301 (2020. https://doi.org/10.1002/widm.1301 [Google Scholar]
  14. A. Mahbod, G. Schaefer, R. Ecker, et al., Fusing fine-tuned deep features for skin lesion classification. Comput. Med. Imaging Graph. 84, 101765 (2020. https://doi.org/10.1016/j.compmedimag.2020.101765 [Google Scholar]
  15. M. Rashid, M.A. Amin, A. Ali, A.S. Imran, Deep learning for skin disease classification: Taxonomy review, open issues, and challenges. Comput. Biol. Med. 150, 106081 (2022. https://doi.org/10.1016/j.compbiomed.2022.106081 [Google Scholar]
  16. M.M. Musthafa, et al., Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification. BMC Med. Imaging 24, 201 (2024. https://doi.org/10.1186/s12880-024-01356-8 [Google Scholar]
  17. A.S. Al-Waisy, et al., A deep learning framework for automated early diagnosis and classification of skin cancer lesions in dermoscopy images. Sci. Rep. 15, 31234 (2025. https://doi.org/10.1038/s41598-025-15655-9 [Google Scholar]
  18. B. Özdemir, I. Pacal, A robust deep learning framework for multiclass skin cancer classification. Sci. Rep. 15, 4938 (2025. https://doi.org/10.1038/s41598-025-89230-7 [Google Scholar]
  19. J. Howard, S. Gugger, Fastai: A layered API for deep learning. Information 11(2), 108 (2020. https://doi.org/10.3390/info11020108 [Google Scholar]
  20. E. Topol, High-performance medicine: AI's role in healthcare. Nat. Med. (2023. https://doi.org/10.1038/s41591-023-02561-9 [Google Scholar]
  21. K. Khushbu, Skin disease classification dataset. Mendeley Data, V1 (2024. https://doi.org/10.17632/3hckgznc67.1 [Google Scholar]
  22. S.M.S.I. Badhon, S.A. Khushbu, N.C. Saha, K.S.M.T.H. Hossain, A.H. Anik, M.A. Ali, Explainable AI for skin disease classification using Grad-CAM and transfer learning to identify contours. Preprints (2024. https://doi.org/10.20944/preprints202407.2556.v1 [Google Scholar]

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