Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Sotoudeh, Houman; Tabatabaei, Mohsen; Tasorian, Baharak; Tavakol, Kamran; Sotoudeh, Ehsan; Moini, Abdol Latif

doi:10.5455/aim.2020.28.190-195

Cited by 11 publications

(7 citation statements)

References 28 publications

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“…[ 9 ]. The same architecture also detected pneumonia due to COVID-19 with an accuracy of 96.7% [ 10 ] and COVID-19 from x-ray images with an accuracy of 96.30% [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features

Pahar

Klopper

Warren

et al. 2022

Computers in Biology and Medicine

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We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50. These pre-trained networks are subsequently either fine-tuned using smaller datasets of coughing with COVID-19 labels in the process of transfer learning, or are used as bottleneck feature extractors. Results show that a Resnet50 classifier trained by this transfer learning process delivers optimal or near-optimal performance across all datasets achieving areas under the receiver operating characteristic (ROC AUC) of 0.98, 0.94 and 0.92 respectively for all three sound classes: coughs, breaths and speech. This indicates that coughs carry the strongest COVID-19 signature, followed by breath and speech. Our results also show that applying transfer learning and extracting bottleneck features using the larger datasets without COVID-19 labels led not only to improved performance, but also to a marked reduction in the standard deviation of the classifier AUCs measured over the outer folds during nested cross-validation, indicating better generalisation. We conclude that deep transfer learning and bottleneck feature extraction can improve COVID-19 cough, breath and speech audio classification, yielding automatic COVID-19 detection with a better and more consistent overall performance.

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“…[ 9 ]. The same architecture also detected pneumonia due to COVID-19 with an accuracy of 96.7% [ 10 ] and COVID-19 from x-ray images with an accuracy of 96.30% [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features

Pahar

Klopper

Warren

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…However, Resnet50 is used, in [7], to detect COVID-19 from computed tomography (CT) images with an accuracy of 96.23%. The same architecture for detecting COVID-19 was shown, proposed in [8], an accuracy of 96.7% and for detecting COVID-19 from X-ray images an accuracy of 96.30% [9]. Authors in [10] proposed a DNN to detect COVID-19 from x-ray images by applying the transfer learning approach.…”

Section: Introductionmentioning

confidence: 91%

COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach

Khriji

Ammari

Messaoud

et al. 2021

2021 29th Conference of Open Innovations Association (FRUCT)

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The World Health Organization has declared that the new Coronavirus disease has become a pandemic since March 2020. It consists of an emerging viral infection with respiratory swelling that can progress to atypical pneumonia. In fact, experts stress the early detection importance of those infected with COVID-19 virus. In this way, the infected patients will be isolated from others, and then prevent the virus spread. However, prompt assessment of breathing patterns is important for many medical emergencies. We present, in this paper, a deep learning technique-based COVID-19 cough and breath analysis that can recognize positive COVID-19 cases from both negative and healthy COVID-19 cough and breath recorded on smartphones or wearable sensors. Firstly, audio signals, as well as cough and breath, will be preprocessed to remove noise. After that, deep features will be extracted using the deep Long Term Short Memory (LSTM) model. Finally, the recognition step will be performed exploiting extracted audio features. Numerical results prove the efficiency of the proposed deep model in terms of high accuracy level and low loss value compared to the other techniques.

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“…AI solutions are helping the radiologist to analyse the patterns of the radiology image and assess the probability of infection [31] , [32] . A VGG Convolution Neural Network was presented in [33] that can aid the clinical decision support system for early detection of the disease.…”

Section: Robotics and Ai Technologies In Covid-19 Healthcarementioning

confidence: 99%

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

Sarker

Jamal

Ahmed

et al. 2021

Robotics and Autonomous Systems

115

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The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.

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Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Cited by 11 publications

References 28 publications

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features

COVID-19 Recognition Based on Patient's Coughing and Breathing Patterns Analysis: Deep Learning Approach

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

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