Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network

Gan, Fengjiao; Luo, Chenggao; Liu, Xingyue; Wang, Hongqiang; Peng, Long

doi:10.3390/app10082661

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…However, it is difficult to resolve the target under a low signal-to-noise ratio (SNR), and the imaging process is time consuming. In this study [2], the authors provided an efficient solution to tackle this problem. A convolution neural network (CNN) was leveraged to develop an off-line, end-to-end imaging network whose structure is highly parallel and free of iterations.…”

Section: Fast Terahertz Coded-aperture Imaging Based On Convolutional Neural Network By Gan Et Almentioning

confidence: 99%

Special Issue: Novel Approaches for Nondestructive Testing and Evaluation

Lee

Berkache

2022

Applied Sciences

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Section: Fast Terahertz Coded-aperture Imaging Based On Convolutional Neural Network By Gan Et Almentioning

confidence: 99%

Special Issue: Novel Approaches for Nondestructive Testing and Evaluation

Lee

Berkache

2022

Applied Sciences

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“…Convolutional neural networks (CNNs) [23,26] are a class of neural networks which work on the principle of deep learning. A basic CNN architecture consists of alternate layers of convolutional and pooling followed by one or more fully connected layers at the final stage.…”

Section: Shallow Convolutional Neural Networkmentioning

confidence: 99%

Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays

et al. 2021

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Among radiological imaging data, Chest X-rays (CXRs) are of great use in observing COVID-19 manifestations. For mass screening, using CXRs, a computationally efficient AI-driven tool is the must to detect COVID-19-positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19-positive cases using CXRs, with no false negatives. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models. The shallow CNN-tailored architecture was validated using 321 COVID-19-positive CXRs. In addition to COVID-19-positive cases, another set of non-COVID-19 5856 cases (publicly available, source: Kaggle) was taken into account, consisting of normal, viral, and bacterial pneumonia cases. In our experimental tests, to avoid possible bias, 5-fold cross-validation was followed, and both balanced and imbalanced datasets were used. The proposed model achieved the highest possible accuracy of 99.69%, sensitivity of 1.0, where AUC was 0.9995. Furthermore, the reported false positive rate was only 0.0015 for 5856 COVID-19-negative cases. Our results stated that the proposed CNN could possibly be used for mass screening. Using the exact same set of CXR collection, the current results were better than other deep learning models and major state-of-the-art works.

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“…It is therefore vital to generate the training data using the actual physical model of a radar system under its relevant system parameters. An interesting work was presented in [54] where deep learning was facilitated as an enabling technique to solve the image reconstruction problem at terahertz (THz) frequencies. It should be mentioned that this is a fundamentally different concept from the classification problem studied in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…It should be mentioned that this is a fundamentally different concept from the classification problem studied in this paper. Moreover, because the work presented in [54] was not experimentally tested, it is not possible assess the accuracy and applicability of [54] for practical applications. For the security-screening application we consider, this poses a significant disadvantage.…”

Section: Related Workmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

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Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network

Cited by 8 publications

References 36 publications

Special Issue: Novel Approaches for Nondestructive Testing and Evaluation

Special Issue: Novel Approaches for Nondestructive Testing and Evaluation

Shallow Convolutional Neural Network for COVID-19 Outbreak Screening Using Chest X-rays

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

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