# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 4 Solving the Inverse Problem
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**Reference** & **Algorithm** & **Remarks** \\ \hline [PERSON] et al. & & An algebraic NN (Neural Network) is used for the reconstruction of the electrical resistance tomography. It converts image reconstruction into a problem of solving strictly linear equations. The approach showed good convergence and small reconstruction error. \\ \hline [PERSON] et al. & & A neural network is used to replace the conventional back-projection algorithm for the image reconstruction of the electrical capacitance tomography images. The improved backpropagation algorithm trains a two-layer perceptron network and it demonstrated better image resolution. \\ \hline [PERSON] et al. & ANN-PSO & PSO is used to improve the training of the artificial neural network (ANN) for solving the EIT inverse problem. \\ \hline [PERSON] et al. & PSO-NN & PSO is used for the training of NN to solve the limitations in the forward problem of the EIT. \\ \hline [PERSON] et al. & RBF neural network is used for the image reconstruction of the electrical capacitance tomography. The modified NN improved the finite element plotting pattern, data normalization, and input layers. \\ \hline [PERSON] et al. & RBF NN & A radial basis function neural network (RBF-NN) is used for the image reconstruction of the electrical resistance tomography. The algorithm compared with the other image reconstruction algorithms and shows high convergence and accuracy. \\ \hline [PERSON] et al. & RBF neural network is used for the image reconstruction of the Electrical Capacitance Tomography (ECT). For optimizing the centers and widths of the hidden units of RBF networks, an adaptive genetic algorithm is used. Training for the weights of the neural network is performed by using Tikhonov regularization. The method has increased image quality. \\ \hline [PERSON] et al. & BP NN & A neural network is used for the two-phase flow electrical capacitance tomography problem. \\ \hline [PERSON] et al. & BP Neural Network & BP neural network is used for the image reconstruction of the electrical capacitance tomography. The method is used to achieve the conditions of flow regime identification and emphatically examined median filter to recognize image improvement. \\ \hline [PERSON] et al. & RBF NN & RBF neural network for eight electrodes electrical capacitance tomography system is presented. The training of the network is performed by using the genetic algorithm merge with the nearest neighbor clustering method. \\ \hline [PERSON] et al. & RBF NN and & A RBF-NN algorithm is used to achieve the conditions of flow regime identification for image reconstruction for the ECT system. An adaptive wavelet filter image reconstruction algorithm depends upon RBF that belongs to a space-frequency analysis technique appropriate for image feature enhancement is studied to improve the reconstruction precision. The algorithm gives good quality of reconstructed images, noise reduction, and edges detection. \\ \hline [PERSON] et al. & RBF neural networks are used for the sixteen-electrode ECT system. It is used to transform the electrode capacitance measurements into the permittivity distributions in the image domain. The number of hidden nodes in RBF-NN are determined by using a maximal matrix element approach and the center width of the RBF function is determined by using the nearest neighbor-clustering method. The image reconstructed is better in quality than the linear back projection algorithm. \\ \hline [PERSON] et al. & RBF neural network is used for the eight-electrode ECT system. Training of the network is performed by combining genetic algorithm along with nearest neighbor clustering algorithm. \\ \hline [PERSON] et al. & Multi-layer NN & A two multi-layer neural network model for image reconstruction of two-phase flow electrical capacitance tomography is used to determine the dimension of the input vectors. The transfer functions of the neurons in the WNN are wavelet base functions that are determined by retracting and translation factors. The BP algorithm performs training of the WNN also self-adaptive learning rate and momentum coefficient are used to speed up the learning procedure. \\ \hline [PERSON] et al. & Neural Networks and Deep Learning & Deep Learning methods along with the NN are used for the electrical impedance tomography. A multilayer perceptron neural network is used to detect the position of the object. \\ \hline \hline \end{tabular}
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