A neural network approach to microwave imaging

Wang, Yuanmei; Gong, Xiaojin

doi:10.1002/1098-1098(2000)11:3<159::aid-ima1000>3.0.co;2-o

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Wang et al 110 presented a neural network-based approach for MWI in medical diagnosis for the first time. The main objective is to reconstruct the complex permittivity of biological tissues illuminated by transverse magnetic (TM) incident waves.…”

Section: History Of Ai-assisted Mwi In Clinical Setting and Researchmentioning

confidence: 99%

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Khalid,

Zubair,

Mehmood

et al. 2024

npj Imaging

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In recent years, microwave imaging (MWI) has emerged as a non-ionizing and cost-effective modality in healthcare, specifically within medical imaging. Concurrently, advances in artificial intelligence (AI) have significantly augmented the capabilities of medical imaging tools. This paper explores the intersection of these two domains, focusing on the integration of AI algorithms into MWI techniques to elevate accuracy and overall performance. Within the scope of existing literature, representative prior works are compared concerning the application of AI in both the “MWI for Healthcare Applications" and “Artificial Intelligence Assistance In MWI" sections. This comparative analysis sheds light on the diverse approaches employed to enhance the synergy between AI and MWI. While highlighting the state-of-the-art technology in MWI and its historical context, this paper delves into the historical taxonomy of AI-assisted MWI, elucidating the evolution of intelligent systems within this domain. Moreover, it critically examines prominent works, providing a nuanced understanding of the advancements and challenges encountered. Addressing the limitations and challenges inherent in developing AI-assisted MWI systems like Generalization to different conditions, Generalization to different conditions, etc the paper offers a brief synopsis of these obstacles, emphasizing the importance of overcoming them for robust and reliable results in actual clinical environments. Finally, the paper not only underscores the current advancements but also anticipates future innovations and developments in utilizing AI for MWI applications in healthcare.

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Section: History Of Ai-assisted Mwi In Clinical Setting and Researchmentioning

confidence: 99%

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Khalid,

Zubair,

Mehmood

et al. 2024

npj Imaging

View full text Add to dashboard Cite

show abstract

“…Neural network approaches in MWT have been applied in various non-medical applications [41,19,8,40,6,29] and an earlier implementation of neural networks in solving an inverse problem in electromagnetics was presented in [10] where the Hopfield neural network was exploited to solve Fredholm integral equation involving reconstruction of material properties of multilayered media. A three layer artificial neural network (ANN) system was implemented in [4] for determining the moisture content in wheat and in [31] microwave reflection technique was used as a stimuli to the ANN built on an error backpropagation algorithm with momentum and adaptive learning techniques to predict the moisture content of commercially important biomass.…”

Section: Introductionmentioning

confidence: 99%

Estimation of moisture content distribution in porous foam using microwave tomography with neural networks

Lähivaara,

Yadav,

Link

et al. 2020

Preprint

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The use of microwave tomography (MWT) in an industrial drying process is demonstrated in this feasibility study with synthetic measurement data. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam during the microwave drying process. Such moisture information is crucial in developing control strategies for controlling the microwave power for selective heating. In practice, a reconstruction time less than one second is desired for the input response to the controller. Thus, to solve the estimation problem related to MWT, a neural network based approach is applied to fulfill the requirement for a real-time reconstruction. In this work, a database containing different moisture content distribution scenarios and corresponding electromagnetic wave responses are build and used to train the machine learning algorithm. The performance of the trained network is tested with two additional datasets.

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Estimation of Moisture Content Distribution in Porous Foam Using Microwave Tomography With Neural Networks

Lähivaara

Yadav

Link

et al. 2020

IEEE Trans. Comput. Imaging

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A neural network approach to microwave imaging

Cited by 3 publications

References 23 publications

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence

Estimation of moisture content distribution in porous foam using microwave tomography with neural networks

Estimation of Moisture Content Distribution in Porous Foam Using Microwave Tomography With Neural Networks

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