Improving skin cancer detection through learning model and IoT technology

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.