SkinSight AI: Skin Cancer Prediction System Using Deep Learning and Explainable Artificial Intelligence

The worldwide prevalence of different diseases and viruses continues to have an impact on societies, intensified by changing lifestyle and environmental factors. The use of modern technology including artificial intelligence (AI) has transformed disease diagnostics, allowing for quick and accurate identification. This work aims to classify images depicting seven different skin diseases by using explainable AI (XAI) together with Convolutional Neural Networks (CNNs). Integrating XAI improves interpretability and offers a thorough understanding of the model's predictions by augmenting the CNN architecture. Leveraging the integration of CNN and XAI technologies, our model demonstrates good precision in the identification of skin diseases, enabling dermatologists to formulate improved, and data-driven decisions with increased accuracy. A rigorous assessment using performance metrics demonstrates consistent performance, with an accurate range of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 0 \%}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 6 \%}$</tex>. The substantial capability of our multilayered CNN architecture in accurately classifying skin diseases is further enhanced by the interpretation provided by XAI.