Fractal Intelligence for Social Good: An Integrated Study

Abstract

Skin cancer remains one of the most common yet preventable cancers worldwide. However, its diagnosis continues to reveal significant technological and social inequalities. This study examines how fractal analysis, particularly the Hausdorff Dimension (HD) which can serve both as a mathematical tool and a socially responsive framework for improving equity in skin cancer detection. Using a dataset of 155 dermoscopic images, HD-based features were integrated with a MATLAB neural-network classifier, achieving a baseline diagnostic accuracy of 78%. The model was then conceptually expanded using simulated crosscultural datasets to evaluate fairness, accessibility, and inclusiveness. 87.4% was achieved on the expanded, simulated dataset. Accuracy of 87.4% indicate that HD descriptors are able to capture the geometric irregularities of malignant lesions more objectively than conventional visual methods, offering a pathway toward earlier, non -invasive, and cost-efficient screening. From a social - science perspective, the convergence of artificial intelligence (AI) and fractal geometry highlights the ethical need to democratise healthcare technologies. Ultimately, this study positions “fractal intelligence” as a form of social innovation and translating computational precision into more equitable public-health outcomes.