A Smart IoT-Image Processing System for Real-Time Skin Cancer Detection

Authors

  • Sunny Kumar
  • Apoorva Dwivedi
  • Yusuf Perwej
  • Moazzam Haidari
  • Siddharth Singh
  • Nagarajan Gurusamy

Keywords:

IoT, Skin Cancer Detection, Image Processing, Real-Time Monitoring, Deep Learning, Smart Healthcare

Abstract

Skin cancer represents a significant global health issue, with early detection being crucial for effective treatment and improved patient outcomes. Traditional methods of skin cancer diagnosis, including clinical examination and biopsy, are often invasive, time-consuming, and prone to human error. This paper proposes a smart IoT-based image processing system for real-time skin cancer detection, aimed at overcoming the limitations of current methods. The system integrates Internet of Things (IoT) devices for continuous monitoring and utilizes deep learning algorithms for accurate image analysis. The proposed system captures dermoscopic images, processes them using a convolutional neural network (CNN), and provides real-time classification of skin lesions into malignant or benign categories. Key findings show that the system achieved high accuracy and sensitivity, with a significant reduction in detection time compared to traditional approaches. The system's portability and real-time capabilities make it suitable for use in remote areas and for telemedicine applications, offering an accessible and reliable solution for early skin cancer detection. The results indicate that such a system could significantly improve the efficiency of skin cancer screening and contribute to better patient outcomes in clinical practice.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.

Wang, H., & Liu, X. (2019). Internet of Things-based skin cancer detection using deep learning algorithms. IEEE Access, 7, 36504-36512.

Avgerinakis, K. (2017). Deep learning for skin cancer detection: A survey. Artificial Intelligence in Medicine, 80, 19-30.

Jafari, M., & Esfahanian, V. (2018). An IoT-based system for skin cancer detection using dermoscopic images. 2018 IEEE International Conference on Communications (ICC), 1-6.

Hussain, M., & Akram, M. (2020). IoT-based framework for early detection of skin cancer. Journal of Medical Systems, 44(6), 1-9.

Dogan, S., & Ozturk, Y. (2019). Application of deep learning in skin cancer detection: A review. Medical & Biological Engineering & Computing, 57(4), 789-802.

Shen, Y., Liu, C., & Li, J. (2021). A deep learning approach for skin cancer classification. International Journal of Computational Intelligence Systems, 14(1), 112-120.

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR), 1-14.

Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431-3440.

Kushwaha, S., & Singh, R. (2020). A review of IoT-based systems for healthcare applications. Computers in Biology and Medicine, 121, 103782.

Fitzpatrick, T. B. (1988). The validity and practicality of sun-reactive skin types I through VI. Archives of Dermatology, 124(6), 869-871.

Cai, Y., & Wei, L. (2020). Multi-class skin cancer detection using deep learning. Journal of Healthcare Engineering, 2020, 1-9.

Pires, R. M. B., & Garcia, E. M. (2018). A deep learning model for real-time skin cancer diagnosis. International Journal of Imaging Systems and Technology, 28(1), 63-74.

Li, X., & Zhang, X. (2021). Development of a deep learning model for early detection of melanoma from dermoscopic images. Frontiers in Public Health, 9, 597410.

Liu, S., & Xie, L. (2021). Real-time skin cancer detection using deep learning and IoT technology. Sensors, 21(4), 1117.

Chen, Y., & Zhao, M. (2020). An IoT-based system for skin cancer diagnosis. Journal of Healthcare Engineering, 2020, 1-9.

Akin, B., & Uysal, M. (2019). Comparative analysis of image processing techniques for skin cancer detection. Computational Intelligence and Neuroscience, 2019, 1-9.

Raj, S., & Aslam, N. (2020). Review of convolutional neural networks for skin cancer detection. Journal of Computer Science and Technology, 35(3), 438-447.

Yun, S., & Lee, D. (2020). Efficient deep learning for skin cancer detection using convolutional neural networks. Computers in Biology and Medicine, 123, 103850.

Downloads

Published

2025-04-22

How to Cite

1.
Kumar S, Dwivedi A, Perwej Y, Haidari M, Singh S, Gurusamy N. A Smart IoT-Image Processing System for Real-Time Skin Cancer Detection. J Neonatal Surg [Internet]. 2025Apr.22 [cited 2025Oct.10];14(14S):823-31. Available from: https://jneonatalsurg.com/index.php/jns/article/view/4330

Issue

Vol. 14 No. 14S (2025): Journal of Neonatal Surgery

Section

Original Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.