Estimation of tumor parameters using neural networks for inverse bioheat problem
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ireland Ltd
Abstract
Background and objective: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. Methods: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. Results: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. Conclusions: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters. © 2021
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Keywords
3D modeling, Cell proliferation, Diagnosis, Diseases, Errors, Feedforward neural networks, Inverse problems, Mean square error, Network architecture, Parameter estimation, Temperature control, Bio-heat, Bio-heat transfer, Feedforward neural network, Feedforwards, Inverse estimation, Neural network modelling, Neural-networks, Pennes equation, Temperature profiles, Tumor parameter, Tumors, Article, artificial neural network, bioheat transfer, body temperature, breast cancer, breast tissue, cancer staging, data analysis software, diagnostic accuracy, feed forward neural network, heat transfer, mathematical model, measurement accuracy, measurement error, prediction, quantitative analysis, root mean square error, simulation, tumor diagnosis, tumor localization, tumor volume, breast, breast tumor, computer simulation, human, Breast, Breast Neoplasms, Computer Simulation, Humans, Neural Networks, Computer
Citation
Computer Methods and Programs in Biomedicine, 2021, 205, , pp. -