Skin cancer has become a serious public health issue worldwide. The number of cases is rising due to higher UV exposure and changing lifestyle patterns. Early detection is the best way to save lives. However, many areas still lack specialized doctors and equipment needed for a quick diagnosis. In this paper, we develop and test an automated skin cancer detection method based on standard smartphone photography. It provides a practical solution for resource-limited settings by removing the need for expensive clinical dermoscopy equipment, making diagnosis accessible in areas with limited infrastructure. The system was trained and tested on PAD-UFES-20, which contains 2,298 clinical smartphone... More >