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--Pad\'e 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.
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