530
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Effective Skin Cancer Diagnosis Through Federated Learning and Deep Convolutional Neural Networks

, &
Article: 2364145 | Received 27 Jan 2024, Accepted 30 May 2024, Published online: 06 Jun 2024

References

  • Abarca, J. M. H., and A. J. P. Chávez. 2023. Malignant Nail Melanoma in a case report. Journal of Pharmaceutical Negative Results 14 (2):67–27.
  • Abdou, M. 2022. Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Computing and Applications 34 (8):5791–812. doi:10.1007/s00521-022-06960-9.
  • Ali, R., Hardie, R.C., De Silva, M.S. and T.M. Kebede. 2019. Skin lesion segmentation and classification for ISIC 2018 by combining deep CNN and handcrafted features. arXiv Preprint arXiv:05730.
  • Bibi, S., M. A. Khan, J. H. Shah, R. Damaševičius, A. Alasiry, M. Marzougui, M. Alhaisoni, and A. Masood. 2023. MSRNet: Multiclass skin lesion recognition using additional residual block based fine-tuned deep models information fusion and best feature selection. Diagnostics 13 (19):3063. doi:10.3390/diagnostics13193063.
  • Carbonell, M. C., and R. V. Peña. 2021. Convolutional neural network architecture for skin cancer diagnosis. European Journal of Molecular Clinical Medicine 8 (3):2819–33.
  • Dillshad, V., M. A. Khan, M. Nazir, O. Saidani, N. Alturki, and S. Kadry. 2023. D2LFS2Net: Multi‐class skin lesion diagnosis using deep learning and variance‐controlled Marine Predator optimisation: An application for precision medicine. CAAI Transactions on Intelligence Technology. doi:10.1049/cit2.12267.
  • Dobre, E.-G., M. Surcel, C. Constantin, M. A. Ilie, A. Caruntu, C. Caruntu, and M. Neagu. 2023. Skin cancer pathobiology at a glance: A focus on imaging techniques and their potential for improved diagnosis and surveillance in clinical cohorts. International Journal of Molecular Sciences 24 (2):1079. doi:10.3390/ijms24021079.
  • Dutta, A., M. Kamrul Hasan, and M. Ahmad. 2021. Skin lesion classification using convolutional neural network for melanoma recognition. Proceedings of International Joint Conference on Advances in Computational Intelligence: IJCACI 2020, Singapore. Springer.
  • Gouda, W., N. U. Sama, G. Al-Waakid, M. Humayun, and N. Z. Jhanjhi. 2022. Detection of skin cancer based on skin lesion images using deep learning. In Healthcare, ed. C. Joaquim and G. Danielec, 2–18. Basel, Switzerland: MDPI.
  • Karri, M., C. S. R. Annavarapu, and U. R. Acharya. 2023. Skin lesion segmentation using two-phase cross-domain transfer learning framework. Computer Methods Programs in Biomedicine 231:107408. doi:10.1016/j.cmpb.2023.107408.
  • Khamparia, A., P. K. Singh, P. Rani, D. Samanta, A. Khanna, and B. Bhushan. 2021. An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning. Transactions on Emerging Telecommunications Technologies 32 (7):e3963. doi:10.1002/ett.3963.
  • Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A. and Smith, V 2020. Federated optimization in heterogeneous networks. Proceedings of Machine Learning Systems 2:429–50.
  • Maharana, K., S. Mondal, and B. Nemade. 2022. A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings 3 (1):91–99.
  • McMahan, B., Moore, E., Ramage, D., Hampson, S. and Arcas, B.A. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, 1273–1282. Florida, USA: MLR, JMLR & W&CP.
  • Mendonça, T., Ferreira, P.M., Marçal, A.R., Barata, C., Marques, J.S., Rocha, J. and Rozeira, J. 2015. Ph2: A public database for the analysis of dermoscopic images. Dermoscopy Image Analysis 10:419.
  • Milton, M. A. A. 2019. Automated skin lesion classification using ensemble of deep neural networks in ISIC 2018: Skin lesion analysis towards melanoma detection challenge. arXiv Preprint arXiv:10802.
  • Moldovanu, S., F. A. Damian Michis, K. C. Biswas, A. Culea-Florescu, and L. Moraru. 2021. Skin lesion classification based on surface fractal dimensions and statistical color cluster features using an ensemble of machine learning techniques. Cancers 13 (21):5256. doi:10.3390/cancers13215256.
  • Moldovanu, S., M. Miron, C.-G. Rusu, K. C. Biswas, and L. Moraru. 2023. Refining skin lesions classification performance using geometric features of superpixels. Scientific Reports 13 (1):11463. doi:10.1038/s41598-023-38706-5.
  • Organization, W. H. 2017. Radiation: Ultraviolet (UV) radiation and skin cancer.
  • Parker, E. R. 2021. The influence of climate change on skin cancer incidence–A review of the evidence. International Journal of Women’s Dermatology 7 (1):17–27. doi:10.1016/j.ijwd.2020.07.003.
  • Sinha, S., and N. Gupta. 2022. A comparative analysis of transfer learning-based techniques for the classification of Melanocytic Nevi. arXiv Preprint arXiv:10972.
  • Sm, J., C. Aravindan, and R. Appavu. 2023. Classification of skin cancer from dermoscopic images using deep neural network architectures. Multimedia Tools Applications 82 (10):15763–78. doi:10.1007/s11042-022-13847-3.
  • Vaccarella, S., D. Georges, F. Bray, O. Ginsburg, H. Charvat, P. Martikainen, H. Brønnum-Hansen, P. Deboosere, M. Bopp, M. Leinsalu, et al. 2023. Socioeconomic inequalities in cancer mortality between and within countries in Europe: A population-based study. The Lancet Regional Health - Europe 25. doi:10.1016/j.lanepe.2022.100551.
  • Varma, P. B. S., S. Paturu, S. Mishra, B. S. Rao, P. M. Kumar, and N. V. Krishna. 2022. SLDCNet: Skin lesion detection and classification using full resolution convolutional network-based deep learning CNN with transfer learning. Expert Systems 39 (9):e12944. doi:10.1111/exsy.12944.
  • Venugopal, K., D. Youlden, L. T. Marvelde, R. Meng, J. Aitken, S. Evans, I. Kostadinov, R. Nolan, H. Thomas, and K. D’Onise. 2023. Twenty years of melanoma in Victoria, Queensland, and South Australia (1997–2016). Cancer Epidemiology 83:102321. doi:10.1016/j.canep.2023.102321.
  • Wang, R., J. Xu, Y. Ma, M. Talha, M. S. Al-Rakhami, and A. Ghoneim. 2021. Auxiliary diagnosis of COVID-19 based on 5G-enabled federated learning. IEEE Network 35 (3):14–20. doi:10.1109/MNET.011.2000704.
  • Zhang, Z. 2018. Improved Adam optimizer for deep neural networks. 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS), Banff, AB, Canada. Ieee.