Comparison of mouse embryo deformation modeling under needle injection using analytical Jacobian, nonlinear least square and artificial neural network techniques

Abstract

Analytical Jacobian, nonlinear least square and three layer artificial neural network models are employed to predict deformation of mouse embryos under needle injection, based on experimental data captured from literature. The Maximum Absolute Error (MAE), coefficient of determination (R2), Relative Error of Prediction (REP), Root Mean Square Error of Prediction (RMSEP), Nash–Sutcliffe coefficient of efficiency (Ef) and accuracy factor (Af) are used as the basis for comparison of these three models. Analytical Jacobian, nonlinear least square and ANN models have yielded the correlation coefficient of 0.9985, 0.9964 and 0.9998, respectively. The REP between the models predicted values and experimental observations are 2.8228(%), 4.7647(%) and 0.4698(%) for the analytical Jacobian, nonlinear least square and ANN methods, respectively. Results showed that ANN performed relatively better than the analytical Jacobian and nonlinear least square methods. Findings indicate that the ANN technique can predict mouse embryo dimple depth by needle injection with considerable accuracy and the least error.