Estimation of tumor parameters using neural networks for inverse bioheat problem.

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.

A review study on blood in human coronary artery: Numerical approach.

Computational fluid dynamics (CFD) study of blood flow in human coronary artery is one of the emerging fields of Biomed- ical engineering. In present review paper, Finite Volume Method with governing equations and boundary conditions are briefly discussed for different coronary models. Many researchers have come up with astonishing results related to the various factors (blood viscosity, rate of blood flow, shear stress on the arterial wall, Reynolds number, etc.) affecting the hemodynamic of blood in the right/left coronary artery. The aim of this paper is to present an overview of all those work done by the researchers to justify their work related to factors which hampers proper functioning of heart and lead to Coronary Artery Disease (CAD). Governing equations like Navier-stokes equations, continuity equations etc. are widely used and are solved using CFD solver to get a clearer view of coronary artery blockage. Different boundary conditions and blood properties published in the last ten years are summarized in the tabulated form. This table will help new researchers to work on this area.