Skin cancer remains a major healthcare challenge. Early detection is important because it increases the chances of successful treatment. Deep learning has become an effective approach for automated skin lesion classification. However, many existing studies rely on a single dataset, which limits the diversity of training images and may reduce model generalization. This study proposes a transfer learning framework for binary skin cancer classification using dataset-level fusion. Two public datasets, PAD-UFES-20 and ISIC~2016, are combined to create a fused dataset that contains both clinical smartphone images and dermoscopic images. The images are resized to 224 × 224 pixels, converted to RGB... More >

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 >