MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN

• URL: http://arxiv.org/abs/2102.07911v1
• Date: Tue, 16 Feb 2021 01:42:31 GMT
• Title: MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN
• Abstract summary: We propose a generative adversarial network (GAN) enhanced MIT technique, named MITNet, based on a complex convolutional neural network (CNN) The experimental results on the real-world dataset validate the performance of our technique, which outperforms the state-of-art method by 25.27%.
• Score: 4.053866599861625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic induction tomography (MIT) is an efficient solution for long-term brain disease monitoring, which focuses on reconstructing bio-impedance distribution inside the human brain using non-intrusive electromagnetic fields. However, high-quality brain image reconstruction remains challenging since reconstructing images from the measured weak signals is a highly non-linear and ill-conditioned problem. In this work, we propose a generative adversarial network (GAN) enhanced MIT technique, named MITNet, based on a complex convolutional neural network (CNN). The experimental results on the real-world dataset validate the performance of our technique, which outperforms the state-of-art method by 25.27%.

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