A Generalized Transform Framework for a Nonlinear Model of Cancer Dynamics

This paper develops a generalized transform framework for a logistic–Allee tumor-growth model. The method combines a generalized Laplace transform, Adomian decomposition, Chebyshev–Padé rational reconstruction, and a $\mu$-scaled generalized transform to obtain admissible semi-analytical approximations. Comparisons with experimental tumor-growth data show that the resulting compact representations are stable, admissible, and comparable in error to standard numerical reference solutions.