title: Parameter estimation of breast tumour using dynamic neural network from thermal pattern.
abstract: This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN). Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position) from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients) were promising in terms of the model's potential for retrieving tumour parameters.
authors.affiliation: Energy Engineering and Physics Faculty, Amirkabir University of Technology, Tehran, Iran.
authors.forename: Elham
authors.initials: E
authors.lastname: Saniei
authors.affiliation: Energy Engineering and Physics Faculty, Amirkabir University of Technology, Tehran, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Setayeshi
authors.affiliation: Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Mohammad Esmaeil
authors.initials: ME
authors.lastname: Akbari
authors.affiliation: Medical Thermography Dept., Fanavaran Madoon Ghermez Co. Ltd., Tehran, Iran.
authors.forename: Mitra
authors.initials: M
authors.lastname: Navid
keywords: Breast tumour; Finite element model; Image; Neural network; Pennes bio-heat equation; Thermal pattern