Comparison of Adaptive Neuro-Fuzzy Inference System and Linear Regression models for Body tissue Depth Detection

In this article, a comparison has been made between two different methods fordetecting the depth of body tissues through X-ray energy. These two methods areAdaptive Neural Fuzzy Inference System (ANFIS) and Linear Regression. Humanliver, kidney, and heart tissues were simulated using the MCNPX (Mont Carlo NParticle)code, which is a Monte Carlo code for the simulation of multi-particle radiationtransfer, and were irradiated with X-rays of a specific wavelength. X-ray energyemitted from tissues is considered as input for ANFIS and linear regression models,and depth of tissues is predicted as output. The prediction results were compared withthe real data obtained from the simulation and the error measures such as mean squareerror (MSE) and root mean square error (RMSE) were calculated. It has been shownthat the ANFIS model is more accurate than the linear regression model in detectingthe depth of tissues. The value of MSE and RMSE for the ANFIS model was ۰.۰۰۰۲and ۸.۶۵۹E-۰۹, respectively, and for the linear regression model, it was ۰.۰۶ and ۰.۰۷,respectively. It has also been investigated how the ANFIS model adapts to changesin simulation parameters such as X-ray wavelength or tissue thickness. This papercould be useful for medical applications such as cancer diagnosis or X-ray imagingthat require depth measurements of tissues.

کلیدواژه ها:

Tissue ، Depth ، ANFIS ، Linear Regression ، Detection ، MCNPX