Advancing Skin Cancer Detection: Integrating Attention-Driven Transfer Learning and Autoencoder-Decoder Fusion

The advancement of skin cancer detection has greatly benefited from the integration of spatial attention-driven transfer learning and autoencoder-decoder fusion techniques. Transfer learning allows models to leverage pre-trained knowledge from large, general datasets, which is fine-tuned to recognize specific skin cancer patterns in dermoscopic images. This reduces the reliance on large annotated datasets and improves model performance. Spatial attention mechanisms are then incorporated to enable the model to focus on critical regions, such as the edges and irregularities of skin lesions, enhancing diagnostic accuracy. Additionally, autoencoder-decoder models assist in efficient image segmentation, ensuring that the lesion boundaries are accurately identified and reconstructed. By combining these approaches, the proposed system addresses key challenges such as handling small, subtle lesions and the variability in image quality, ultimately leading to more accurate and robust skin cancer detection systems. This integrated framework shows great potential for improving clinical diagnosis and real-time applications.