Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification

Barros, Bruno; Lacerda, Paulo José Cabral; Albuquerque, Célio; Conci, Aura

doi:10.3390/s21165486

Cited by 42 publications

(26 citation statements)

References 119 publications

(119 reference statements)

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“…As explorations of the role of deep learning in LUS for COVID-19 patients are still underway (e.g., [101][102][103][104]), we believe that in the near future, more research implementing deep learning applications for ultrasound imaging of COVID-19 will be available. These future studies, in combi-nation with the pioneering studies described herein, are expected to provide impactful point-of-care solutions to combat the COVID-19 pandemic.…”

Section: Discussionmentioning

confidence: 99%

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

Zhao

Bell

2022

BME Front

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The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance.

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Section: Discussionmentioning

confidence: 99%

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

Zhao

Bell

2022

BME Front

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“…Hybrid DL algorithms combining backbone CNNs with other units such as the long short-term memory (LSTM) were introduced to improve the model performance. Barros et al tailored a hybrid CNN-LSTM model to classify LUS videos by extracting spatial features with CNNs and then learning the temporal dependence via LSTM [ 87 ]. Their hybrid model reached a higher accuracy of 93% and sensitivity of 97% for COVID-19 cases, compared to other primitive spatial-based models.…”

Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

et al. 2022

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Ultrasound imaging of the lung has played an important role in managing patients with COVID-19–associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

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“…It was built using MATLAB deep learning toolbox and was built from the ground up. The weights of the CNN and LSTM layers were trained at the same time rather than separately or using via transfer learning as the case in [29]. This will allow us to efficiently optimize the number of layers, filters, and LSTM unit numbers which would not be possible with vanilla CNN networks such as VGG16, GoogleNet, AlexNet…etc.…”

Section: The Cnn-lstm Hybrid Architecturementioning

confidence: 99%

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

2022

IJIES

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This paper presents a novel approach for signal detection in non-orthogonal multiple access (NOMA) uplink receivers. We propose converting incoming packets into a stream of 2D image-like vectors. Thereby, converting a signal detection problem into a video classification problem. Our approach is true end-to-end learning where no manual feature engineering and/or pre-processing is required. Detection is done blindly in a joint fashion with no explicit channel estimation and equalization steps required. Successive interference cancellation (SIC) is the default method for detecting NOMA packets, but it requires perfect channel estimation to be maintained. Deep learning approaches have shown great promise. However, they suffer from overfitting and/or poor performance. We show how to improve performance by a better training dataset generation procedure and hyperparameter optimization along with the use of CNN as a feature filter. The CNN-LSTM hybrid network has registered a training and testing accuracies of 88.61 and 85.36 which are higher than the state-of-the-art approaches and its symbol error rate (SER) vs signal-to-noise ratio (SNR) performance is higher by about 9dB than the LSTM approach, maximum likelihood and other standard SIC based approaches like the minimum mean square error (MMSE) and least square (LS). This suggests deep learning-based receivers as strong candidates for the upcoming generations of wireless communication systems.

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Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification

Cited by 42 publications

References 119 publications

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Development of CNN-LSTM Hybrid Deep Learning Network for the Joint Detection of Non-Orthogonal Multiple Access Signals in 5G Uplink Receivers

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