Open Access
Issue
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
Article Number 03015
Number of page(s) 7
Section Information and Technology
DOI https://doi.org/10.1051/itmconf/20268203015
Published online 04 February 2026
  1. O. Akinrinade & C. Du, Skin cancer detection using deep machine learning techniques. Intelligence-Based Medicine. 11, 100191 (2025). doi: https://doi.org/10.1016/j.ibmed.2024.100191 [Google Scholar]
  2. S. Muhammad Saqib, M. Iqbal, M. Tahar Ben Othman, T. Shahazad, Y. Yasin Ghadi, S. Al-Amro, S & T. Mazhar, Lumpy skin disease diagnosis in cattle: A deep learning approach optimized with RMSProp and MobileNetV2. PloS one, 19(8), e0302862 (2024). doi: https://doi.org/10.1371/journal.pone.0302862 [Google Scholar]
  3. A. Begum & S.I. Kalilulah, Deep Learning Advances in Brain Tumor Classification: Leveraging VGG16 and MobileNetV2 for Accurate MRI Diagnostics. In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 1-6 (2024). IEEE. doi:https://doi.org/10.1109/ICPECTS62210.2024.107 80014 [Google Scholar]
  4. K.M. Almustafa, Predictive modeling and optimization in dermatology: Machine learning for skin disease classification, Computers in Biology and Medicine. 189, 109946 (2025). doi:https://doi.org/10.1016/j.compbiomed.2025.1099 46 [Google Scholar]
  5. H. Naseri & A. Safaei. Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review. BMC cancer, 25(1), 75. doi: https://doi.org/10.1186/s12885-024-13423-y (2025) [Google Scholar]
  6. N. Verma, Ranvijay & D.K. Yadav, A comprehensive review on step-based skin cancer detection using machine learning and deep learning methods. Archives of Computational Methods in Engineering, 1-54 (2025). doi: https://doi.org/10.1007/s11831-025-10275-y [Google Scholar]
  7. K. Vayadande, A.A. Bhosle, R.G. Pawar, D.J. Joshi, P.A. Bailke & O. Lohade, O. (2024). Innovative approaches for skin disease identification in machine learning: A comprehensive study. Oral Oncology Reports, 10, 100365 (2024). doi: https://doi.org/10.1016/j.oor.2024.100365 [Google Scholar]
  8. S.S. P.S. Ramesh, S. Narang, N. Praveena, J. Shakila and I. Sudha, Impact of Random Forest and XGBoost Algorithms on Improving Patient Outcomes Compared to Standard Decision-Making Methods in Healthcare Predictive Analytics, 2024 International Conference on Cybernation and Computation (CYBERCOM), Dehradun, India, 694-699 (2024). doi:https://doi.org/10.1109/CYBERCOM63683.2024.10803246 [Google Scholar]
  9. H. Naseri & A.A. Safaei, Diagnosis and prognosis of melanoma from dermoscopy images using machine learning and deep learning: a systematic literature review. BMC cancer, 25(1), 75. doi: https://doi.org/10.1186/s12885-024-13423-y (2025) [Google Scholar]
  10. P. Kavitha, G. Ayyappan, P. Jayagopal, S.K. Mathivanan, S. Mallik, A. Al-Rasheed & B.O. Soufiene, Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach. BMC bioinformatics, 24(1), 458 (2023). doi: https://doi.org/10.1186/s12859-023-05584-7 [Google Scholar]
  11. R. Yadav & A. Bhat, A. A systematic literature survey on skin disease detection and classification using machine learning and deep learning. Multimedia Tools and Applications, 83(32), 78093-78124 (2024). doi: https://doi.org/10.1007/s11042-024-18119-w [Google Scholar]
  12. R. Mittal, F. Jeribi, R. Martin, V. Malik, S.J. Menachery & J. Singh, Dermcdsm: Clinical decision support model for dermatosis using systematic approaches of machine learning and deep learning. IEEE Access, 12, 47319-47337 (2024). doi:https://doi.org/10.1109/ACCESS.2024.3373539 [Google Scholar]
  13. A. Begum, V. Dhilip Kumar, J. Asghar, D. Hemalatha & G. Arulkumaran, G, A combined deep CNN: LSTM with a random forest approach for breast cancer diagnosis. Complexity, 2022(1), 9299621 (2022). doi: https://doi.org/10.1155/2022/9299621 [Google Scholar]
  14. M. Groh, O. Badri, R. Daneshjou, A. Koochek, C. Harris, L.R. Soenksen & R. Picard, R. Deep learning-aided decision support for diagnosis of skin disease across skin tones. Nature Medicine, 30(2), 573-583 (2024). doi: https://doi.org/10.1038/s41591-023-02728-3 [Google Scholar]
  15. https://www.kaggle.com/datasets/ismailpromus/skin-diseases-image-dataset [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.