Shazzat Hossain, Farah A. Mohammadi
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Bio-heat transfer, breast model, tumor parameter estimation,inverse problem, artificial neural network (ANN), patternsearch optimization.
This paper presents a methodology to predict location, size
and hyperactivity level of a breast tumor using temperature
profile over the skin surface of the breast that may be
captured by infrared thermography or numeric simulation.
The estimation methodology includes an evolutionary
technique based on artificial neural network (ANN), an
optimization scheme based on pattern search algorithm
(PSA) with linear constraints and a heat flow analysis on
anatomic-accurate (realistic) breast model using finite
element method (FEM). Laboratory generated datasets
obtained from the FEM are applied to the ANN to associate
underlying tumor with surface temperature of the model.
The ANN training/testing results are in good agreement with
those obtained from numeric method (FEM), thus validates
the network performance. The PSA is applied for generation
of solution vector sets (tumor parameters) within a given
space and the solution sets are employed to produce
simulated datasets using the trained ANN. The best solution
set is determined by minimizing a cost function involving
comparing the target temperature profiles (clinical data) to
those obtained by simulation.