Estimation of tumor parameters using neural networks for inverse bioheat problem. (June 2021)

• Record Type: Journal Article
• Authors: Majdoubi, Jihen; Iyer, Adithya S.; Ashique, Adil M.; Perumal, D. Arumuga; Mahrous, Y.M.; Rahimi-Gorji, Mohammad; Issakhov, Alibek
• Is Part Of: Computer methods and programs in biomedicine. Volume 205(2021)
• Publication Date: 2021-06
• Subjects: Inverse estimation -- Feedforward neural network (FFNN) -- Pennes equation -- Bioheat transfer -- Tumor parameters; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106092
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: These simulations resulted in a temperature distribution profile that could be used to locate and determine the parameters of the cancerous tumor within the breast. A mathematical neural network model was used to estimate the required parameters by computing the data obtained from the bioheat transfer simulations. The architecture of the NN Model in use was optimized by using a specific number of neurons in the hidden layer that would result in the lowest RMSE values. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. A non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters. 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.