Skin Cancer Recognition Using Attention-Based CNN Algorithm

Skin cancer is a common and potentially lethal condition, requiring precise and effective diagnosis techniques. This research introduces a sophisticated method for skin cancer detection with an attention-enhanced convolutional neural network (CNN) model, emphasizing the Xception algorithm. The proposed method emphasizes attention mechanisms inside the CNN to preferentially concentrate on critical lesion regions, hence strengthening feature extraction and augmenting classification accuracy. The system is assessed using the HAM10000 dataset, demonstrating strong performance across seven categories of skin lesions. A comparative examination with conventional CNN models indicates that attention-based CNNs, particularly Xception, achieve markedly superior accuracy, precision, and recall, underscoring its promise in clinical diagnosis. This method improves early detection of skin cancer and offers a promising pathway for enhancing patient outcomes via dependable, automated diagnosis.