Automated Skin Cancer Detection using Deep Learning with Self-Attention Mechanism

Skin cancer is a prevalent and potentially fatal disease that affects a large number of individuals worldwide. Detecting skin cancer early is vital for effective treatment and positive patient outcomes. In this research, a deep learning (DL) model is presented that utilizes the VGG19 architecture with self-attention modules to automate skin cancer detection. The proposed model achieved an impressive overall accuracy of 91.4% on HAM10000 dataset and outperformed several standard classification models, proving that incorporating self-attention modules into convolutional neural networks can significantly enhance skin cancer detection systems' efficiency. Nonetheless, the model has certain limitations, such as a limited dataset and lower accuracy for the melanoma class. Thus, further studies are required to improve the model's performance, which could potentially save lives by allowing for early detection and treatment of melanoma. This proposed approach has shown promising results for automating skin cancer detection, and the potential impact of this research is significant for developing more accurate and reliable skin cancer detection systems.