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Yuan Y, Hou S, Wu X, Wang Y, Sun Y, Yang Z, Yin S, Zhang F. Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma. Asian J Surg 2024:S1015-9584(24)00401-9. [PMID: 38453612 DOI: 10.1016/j.asjsur.2024.02.140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/09/2024] Open
Abstract
PURPOSE It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs) in papillary thyroid carcinoma (PTC) patients. This study aims to develop deep-learning models for the automatic segmentation and classification of LNM on original ultrasound images. METHODS This study included 1000 lateral cervical LN ultrasound images (consisting of 512 benign and 558 metastatic LNs) collected from 728 patients at the Chongqing General Hospital between March 2022 and July 2023. Three instance segmentation models (MaskRCNN, SOLO and Mask2Former) were constructed to segment and classify ultrasound images of lateral cervical LNs by recognizing each object individually and in a pixel-by-pixel manner. The segmentation and classification results of the three models were compared with an experienced sonographer in the test set. RESULTS Upon completion of a 200-epoch learning cycle, the loss among the three unique models became negligible. To evaluate the performance of the deep-learning models, the intersection over union threshold was set at 0.75. The mean average precision scores for MaskRCNN, SOLO and Mask2Former were 88.8%, 86.7% and 89.5%, respectively. The segmentation accuracies of the MaskRCNN, SOLO, Mask2Former models and sonographer were 85.6%, 88.0%, 89.5% and 82.3%, respectively. The classification AUCs of the MaskRCNN, SOLO, Mask2Former models and sonographer were 0.886, 0.869, 0.90.2 and 0.852 in the test set, respectively. CONCLUSIONS The deep learning models could automatically segment and classify lateral cervical LNs with an AUC of 0.92. This approach may serve as a promising tool to assist sonographers in diagnosing lateral cervical LNMs among patients with PTC.
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Affiliation(s)
- Yuquan Yuan
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Shaodong Hou
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Xing Wu
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yuteng Wang
- College of Computer Science, Chongqing University, Chongqing, China
| | - Yiceng Sun
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China
| | - Zeyu Yang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China.
| | - Supeng Yin
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
| | - Fan Zhang
- Department of Breast and Thyroid Surgery, Chongqing General Hospital, Chongqing, China; Clinical Medical College, North Sichuan Medical College, Nanchong, Sichuan, China; Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
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Liu T, Jiang T, Liu G, Sun C. Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 24:215. [PMID: 38203077 PMCID: PMC10781382 DOI: 10.3390/s24010215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/25/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in sensor readings. To address this issue, this study aimed to analyze the impact of temperature on GS monitoring sensors and establish a mapping relationship between temperature uncertainty (fluctuations of measurement caused by temperature variation) and temperature variation. By collecting the temperature information and inferring the temperature uncertainty being introduced, this interference can be removed. However, it is crucial to note that in real-world complex scenarios, the relationship between temperature uncertainty and temperature variation is not always a constant positive correlation, which limits the data available for certain periods. Moreover, the limited availability of data presents a challenge when analyzing the complex mapping relationship. To overcome these challenges, a transfer-learning-based algorithm was introduced to develop a more accurate model for predicting temperature uncertainty based on temperature variation, even with limited data. Subsequently, a practical test was conducted to validate the proposed algorithm's performance. The results demonstrated that the algorithm outperformed a simple linear fitting model using the least squares method (LSM), achieving an improvement of up to 21.9%. This outcome highlights the algorithm's potential for enhancing the performance of GS sensors in daytime monitoring and contributing to the safe operation of oil tank facilities and infrastructure health monitoring.
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Affiliation(s)
| | | | | | - Changsen Sun
- College of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian 116024, China; (T.L.); (T.J.); (G.L.)
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Ghabri H, Alqahtani MS, Ben Othman S, Al-Rasheed A, Abbas M, Almubarak HA, Sakli H, Abdelkarim MN. Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers. Sci Rep 2023; 13:17904. [PMID: 37863944 PMCID: PMC10589237 DOI: 10.1038/s41598-023-44689-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023] Open
Abstract
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.
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Affiliation(s)
- Haifa Ghabri
- MACS Laboratory, National Engineering School of Gabes, University of Gabes, 6029, Gabès, Tunisia
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE17RH, UK
| | - Soufiene Ben Othman
- PRINCE Laboratory Research, ISITcom, Hammam Sousse, University of Sousse, Sousse, Tunisia.
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - Hassan Ali Almubarak
- Division of Radiology, Department of Medicine, College of Medicine and Surgery, King Khalid University (KKU), Abha, Aseer, Saudi Arabia
| | - Hedi Sakli
- EITA Consulting, 5 Rue Du Chant des Oiseaux, 78360, Montesson, Montesson, France
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Choi JY, Yoo TK. New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:337. [PMID: 37675304 PMCID: PMC10477620 DOI: 10.21037/atm-23-1598] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/28/2023] [Indexed: 09/08/2023]
Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
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Darwish SM, Abu Shaheen LJ, Elzoghabi AA. A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique. Bioengineering (Basel) 2023; 10:819. [PMID: 37508846 PMCID: PMC10376225 DOI: 10.3390/bioengineering10070819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.
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Affiliation(s)
- Saad M Darwish
- Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, Alexandria 21526, Egypt
| | - Lina J Abu Shaheen
- Department of Computer Information Systems, College of Technology and Applied Sciences, Al-Quds Open University, Deir AL Balah P920, Palestine
| | - Adel A Elzoghabi
- Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, 163 Horreya Avenue, El Shatby, Alexandria 21526, Egypt
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Brock KK, Chen SR, Sheth RA, Siewerdsen JH. Imaging in Interventional Radiology: 2043 and Beyond. Radiology 2023; 308:e230146. [PMID: 37462500 PMCID: PMC10374939 DOI: 10.1148/radiol.230146] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Since its inception in the early 20th century, interventional radiology (IR) has evolved tremendously and is now a distinct clinical discipline with its own training pathway. The arsenal of modalities at work in IR includes x-ray radiography and fluoroscopy, CT, MRI, US, and molecular and multimodality imaging within hybrid interventional environments. This article briefly reviews the major developments in imaging technology in IR over the past century, summarizes technologies now representative of the standard of care, and reflects on emerging advances in imaging technology that could shape the field in the century ahead. The role of emergent imaging technologies in enabling high-precision interventions is also briefly reviewed, including image-guided ablative therapies.
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Affiliation(s)
- Kristy K Brock
- From the Departments of Imaging Physics (K.K.B., J.H.S.), Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Stephen R Chen
- From the Departments of Imaging Physics (K.K.B., J.H.S.), Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Rahul A Sheth
- From the Departments of Imaging Physics (K.K.B., J.H.S.), Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
| | - Jeffrey H Siewerdsen
- From the Departments of Imaging Physics (K.K.B., J.H.S.), Interventional Radiology (S.R.C., R.A.S.), Neurosurgery (J.H.S.), and Radiation Physics (J.H.S.), The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT14.6050 Pickens Academic Tower, Houston, TX 77030-4000
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7
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Yin M, Liang X, Wang Z, Zhou Y, He Y, Xue Y, Gao J, Lin J, Yu C, Liu L, Liu X, Xu C, Zhu J. Identification of Asymptomatic COVID-19 Patients on Chest CT Images Using Transformer-Based or Convolutional Neural Network-Based Deep Learning Models. J Digit Imaging 2023; 36:827-836. [PMID: 36596937 PMCID: PMC9810383 DOI: 10.1007/s10278-022-00754-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 01/04/2023] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) has rapidly spread throughout the world; however, it is difficult for clinicians to make early diagnoses. This study is to evaluate the feasibility of using deep learning (DL) models to identify asymptomatic COVID-19 patients based on chest CT images. In this retrospective study, six DL models (Xception, NASNet, ResNet, EfficientNet, ViT, and Swin), based on convolutional neural networks (CNNs) or transformer architectures, were trained to identify asymptomatic patients with COVID-19 on chest CT images. Data from Yangzhou were randomly split into a training set (n = 2140) and an internal-validation set (n = 360). Data from Suzhou was the external-test set (n = 200). Model performance was assessed by the metrics accuracy, recall, and specificity and was compared with the assessments of two radiologists. A total of 2700 chest CT images were collected in this study. In the validation dataset, the Swin model achieved the highest accuracy of 0.994, followed by the EfficientNet model (0.954). The recall and the precision of the Swin model were 0.989 and 1.000, respectively. In the test dataset, the Swin model was still the best and achieved the highest accuracy (0.980). All the DL models performed remarkably better than the two experts. Last, the time on the test set diagnosis spent by two experts-42 min, 17 s (junior); and 29 min, 43 s (senior)-was significantly higher than those of the DL models (all below 2 min). This study evaluated the feasibility of multiple DL models in distinguishing asymptomatic patients with COVID-19 from healthy subjects on chest CT images. It found that a transformer-based model, the Swin model, performed best.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Xiaolong Liang
- Department of Orthopedics, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
| | - Zilan Wang
- Department of Neurosurgery, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
| | - Yijia Zhou
- Medical School, Soochow University, Suzhou, 215006, Jiangsu, China
| | - Yu He
- Medical School, Soochow University, Suzhou, 215006, Jiangsu, China
| | - Yuhan Xue
- Medical School, Soochow University, Suzhou, 215006, Jiangsu, China
| | - Jingwen Gao
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Jiaxi Lin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Chenyan Yu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Lu Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Xiaolin Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China
| | - Chao Xu
- Department of Radiotherapy, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China
| | - Jinzhou Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, 215006, Jiangsu, China.
- The 23Rd Ward, Yangzhou Third People's Hospital, Yangzhou, 225000, Jiangsu, China.
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Jafari M, Shoeibi A, Khodatars M, Ghassemi N, Moridian P, Alizadehsani R, Khosravi A, Ling SH, Delfan N, Zhang YD, Wang SH, Gorriz JM, Alinejad-Rokny H, Acharya UR. Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review. Comput Biol Med 2023; 160:106998. [PMID: 37182422 DOI: 10.1016/j.compbiomed.2023.106998] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 05/16/2023]
Abstract
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.
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Affiliation(s)
- Mahboobeh Jafari
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Afshin Shoeibi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Navid Ghassemi
- Internship in BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia
| | - Parisa Moridian
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia
| | - Niloufar Delfan
- Faculty of Computer Engineering, Dept. of Artificial Intelligence Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, 2052, Australia; UNSW Data Science Hub, The University of New South Wales, Sydney, NSW, 2052, Australia; Health Data Analytics Program, Centre for Applied Artificial Intelligence, Macquarie University, Sydney, 2109, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Peng L, Fang S, Fan Y, Wang M, Ma Z. A Method of Noise Reduction for Radio Communication Signal Based on RaGAN. SENSORS (BASEL, SWITZERLAND) 2023; 23:475. [PMID: 36617068 PMCID: PMC9823341 DOI: 10.3390/s23010475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction.
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