Scholarlink

<p>A tumour is a mass or lump of tissue that forms when abnormal cells aggregate, posing a serious and harmful health risk. In this study, we develop a fractional-order cancer immune dynamical model to compare tumour cells and effector cells. Our primary objective is to introduce a neural network-based method for solving the fractional-order cancer model. We apply the average and subtraction-based optimisation (ASBO) method to solve the model. ASBO is a three-step optimisation method which gives us accurate results. We analyse it using Levenberg–Marquardt neural networks (LMNNs). The robustness and versatility of the ASBO algorithm enable it to converge rapidly to the ideal values. We examine the interactions between tumour cells and immune cells, thereby simulating a real-world medical research problem that aids model construction. Using the Jacobian matrix, we establish the model’s stability and prove ULAM’s stability. We solve the problem with a neural network containing 20 neurons. The LMNN approach applies supervised learning, where we use <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(80\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>80</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> of the data for training, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(15\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>15</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> for validation and <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(5\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>5</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> for testing. We assess the efficacy and accuracy of LMNNs through regression plots, correlation analysis, histogram curves and function tests. We also employ the Adams–Bashforth–Moulton (ABM) predictor–corrector method to compare with our solutions. We generate all graphical results in MATLAB. The findings of this investigation will support biologists in the treatment of cancer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A study on fractional cancer model using neural network scheme based on PINN–ASBO

  • 期刊论文

  • 发表日期：

    2026年5月2日

查看全文
  • Chumki Banerjee,
  • Sunil Kumar,
  • Shaher Momani

摘要

A tumour is a mass or lump of tissue that forms when abnormal cells aggregate, posing a serious and harmful health risk. In this study, we develop a fractional-order cancer immune dynamical model to compare tumour cells and effector cells. Our primary objective is to introduce a neural network-based method for solving the fractional-order cancer model. We apply the average and subtraction-based optimisation (ASBO) method to solve the model. ASBO is a three-step optimisation method which gives us accurate results. We analyse it using Levenberg–Marquardt neural networks (LMNNs). The robustness and versatility of the ASBO algorithm enable it to converge rapidly to the ideal values. We examine the interactions between tumour cells and immune cells, thereby simulating a real-world medical research problem that aids model construction. Using the Jacobian matrix, we establish the model’s stability and prove ULAM’s stability. We solve the problem with a neural network containing 20 neurons. The LMNN approach applies supervised learning, where we use \(80\%\) 80 % of the data for training, \(15\%\) 15 % for validation and \(5\%\) 5 % for testing. We assess the efficacy and accuracy of LMNNs through regression plots, correlation analysis, histogram curves and function tests. We also employ the Adams–Bashforth–Moulton (ABM) predictor–corrector method to compare with our solutions. We generate all graphical results in MATLAB. The findings of this investigation will support biologists in the treatment of cancer.