Explainable deep learning for skin cancer detection using swish-activated convolutional networks

Skin cancer is among the most frequent and fatal illnesses in the world, but early and correct diagnosis is one of the main challenges because of the complicated visual patterns of skin lesions and the absence of interpretable diagnostic devices. Conventional techniques are largely based on experienced dermatologists, but manual inspection is time-consuming, subjective, and liable to misinterpretation, with high false positive outputs. To address these issues, we present a unique DCNN architecture that employs the Swish activation function to effectively identify complex patterns in the skin lesion dataset. This model has remarkable performance in diagnosing skin cancer, as evidenced by a 98.31% accuracy rate, a 98.12% precision rate, a 98.01% recall rate, and an F1-score of 98.09%. Utilising several localised and global explainable artificial intelligence (XAI) approaches, we evaluate the model's predictions to ensure transparency and reliability in medical contexts. To reconcile the disparity between AI research and its use in healthcare, our findings underscore the necessity of integrating deep learning with explainability. These XAI solutions tackle critical concerns such as inclusiveness, transparency, and error control, providing medical practitioners with a comprehensible and reliable framework for assessing the model's reasoning process. The suggested technique establishes a reliable mechanism to assist physicians in the early and precise identification of skin cancer, while enhancing diagnostic accuracy. Future studies will focus on enhancing the model's computational efficiency and incorporating more datasets to ensure its durability and fairness across various demographic groupings.