Golmankhaneh, Amirreza KhaliliTunç, SümeyyeSchlichtinger, Agnieszka MatyldaAsanza, Dachel MartinezGolmankhaneh, Alireza Khalili2023-11-242023-11-242024Golmankhaneh, A. K., Tunç, S., Schlichtinger, A. M., Asanza, D. M. ve Golmankhaneh, A. K. (2024). Modeling tumor growth using fractal calculus: Insights into tumor dynamics. BioSystems, 235. https://dx.doi.org/10.1016/j.biosystems.2023.1050710303-26471872-8324https://dx.doi.org/10.1016/j.biosystems.2023.105071https://hdl.handle.net/20.500.12511/11869Important concepts like fractal calculus and fractal analysis, the sum of squared residuals, and Aikaike's information criterion must be thoroughly understood in order to correctly fit cancer-related data using the proposed models. The fractal growth models employed in this work are classified in three main categories: Sigmoidal growth models (Logistic, Gompertz, and Richards models), Power Law growth model, and Exponential growth models (Exponential and Exponential-Lineal models)”. We fitted the data, computed the sum of squared residuals, and determined Aikaike's information criteria using Matlab and the web tool WebPlotDigitizer. In addition, the research investigates “double-size cancer” in the fractal temporal dimension with respect to various mathematical models.eninfo:eu-repo/semantics/embargoedAccessFractal AnalysisFractal CalculusFractal Cancer Growth ModelsFractal Gompertz Growth ModelFractal Richards Growth ModelFractal TemporalArticle23510.1016/j.biosystems.2023.105071Q30011135728000012-s2.0-8517677720537944632Q3