| Issue |
EPJ Web Conf.
Volume 343, 2025
1st International Conference on Advances and Innovations in Mechanical, Aerospace, and Civil Engineering (AIMACE-2025)
|
|
|---|---|---|
| Article Number | 05005 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence & Machine Learning in Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534305005 | |
| Published online | 19 December 2025 | |
https://doi.org/10.1051/epjconf/202534305005
Improving skin cancer detection through learning model and IoT technology
1 Department of Computer Science & Engineering, School of Computer Science & Engineering (SOCSE), Sandip University, Nashik, Maharashtra, India
2 Department of Computer Science & Engineering, School of Computer Science & Engineering (SOCSE), Sandip University, Nashik, Maharashtra, India
* Corresponding author: manishasagar3322@email.org
Published online: 19 December 2025
Effective skin cancer detection requires timely and accurate assessment, yet traditional diagnostic methods often depend on specialized resources unavailable in many communities. This project introduces a novel integration of machine learning algorithms and IoT-enabled hardware to build an affordable, accessible system for early skin cancer identification. Leveraging advanced image classification models deployed on lightweight devices such as a Raspberry Pi with a camera module, the solution processes skin lesion images in real time and delivers immediate diagnostic feedback. Evaluation with diverse datasets demonstrates strong performance across varying skin types and environmental conditions. The synergy of AI and IoT enhances diagnostic precision and bridges gaps in remote and under-resourced healthcare environments.
Key words: Skin cancer detection / machine learning / IoT / Raspberry Pi / CNN / deep learning for telemedicine applications
© The Authors, published by EDP Sciences, 2025
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