ARTIFICIAL NEURAL NETWORKS FOR IMPROVED IMAGE RECONSTRUCTION IN ELECTRICAL IMPEDANCE TOMOGRAPHY

Abstract

The research applies the Artificial Neural Network (ANN) method to create images by utilizing Electrical Impedance Tomography (EIT) as its image reconstruction system. The ANN technique demonstrates versatility because its applications extend across different domains, which include classification functions with additional enhancement capabilities and reconstruction procedures. Data receives classifications in multiple domains according to its purpose, where numbers or animals, or signboards represent three examples of categorized data. The enhancement technique functions to both enhance the image quality and remove unwanted noise, and it operates either on 1-D data alone or on 2-D data based on user needs. The image reconstruction process requires both the input and output neurons of the neural network to have the same number for accurate image reconstruction. The technique supports application to signals of 1-D, 2-D, and 3-D dimensions that generate outcome vectors or matrices of identical sizes to the input data. The research implements an ANN to restore visual information from the source data.The document follows a standard organization with chapters starting from the introduction to methods through results before concluding. The first part of the paper delivers a technique summary along with research reviews related to multiple uses of neural networks. The proposed approach receives a detailed explanation throughout the methods part, followed by a result presentation of images before and after the neural network technique usage. The final chapter brings together the conclusions based on the released results, which point toward prospective research agendas.