ArticleIEEE transactions on antennas and propagation2020
Microwave Imaging by Deep Learning Network: Feasibility and Training Method.
Article in IEEE transactions on antennas and propagation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A data-driven approach for risk assessment and material identification of buried objects using microwave measurements and neural networks.Scientific reports · 2025Article
- Threat Assessment of Buried Objects Using Single-Frequency Microwave Measurements.Sensors (Basel, Switzerland) · 2025Article
- A direct learning approach for detection of hotspots in microwave hyperthermia treatments.Medical & biological engineering & computing · 2025Article
- Machine Learning in Microwave Medical Imaging and Lesion Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence.Npj imaging · 2024Review
- Microwave Imaging of Anisotropic Objects by Artificial Intelligence Technology.Sensors (Basel, Switzerland) · 2023Article
- Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.Bioengineering (Basel, Switzerland) · 2023Article
- Mammography using low-frequency electromagnetic fields with deep learning.Scientific reports · 2023Article
- Radar-Based Microwave Breast Imaging Using Neurocomputational Models.Diagnostics (Basel, Switzerland) · 2023Article
- Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models.Biosensors · 2023Article
- A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.Biosensors · 2023Article
- Near-Field Microwave Scattering Formulation by A Deep Learning Method.IEEE transactions on microwave theory and techniques · 2022Article
- A deep learning model to classify and detect brain abnormalities in portable microwave based imaging system.Scientific reports · 2022Article
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Authors and funding
2 authors.
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Abstract
Microwave image reconstruction based on a deep-learning method is investigated in this paper. The neural network is capable of converting measured microwave signals acquired from a 24×24 antenna array at 4 GHz into a 128×128 image. To reduce the training difficulty, we first developed an autoencoder by which high-resolution images (128×128) were represented with 256×1 vectors; then we developed the second neural network which aimed to map microwave signals to the compressed features (256×1 vector). Two neural networks can be combined to a full network to make reconstructions, when both are successfully developed. The present two-stage training method reduces the difficulty in training deep learning networks (DLN) for inverse reconstruction. The developed neural network is validated by simulation examples and experimental data with objects in different shapes/sizes, placed in different locations, and with dielectric constant ranging from 2~6. Comparisons between the imaging results achieved by the present method and two conventional approaches: distorted Born iterative method (DBIM) and phase confocal method (PCM) are also provided.
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