CoughGAN: Generating Synthetic Coughs that Improve Respiratory Disease Classification

Ramesh, Vishwajith; Vatanparvar, Korosh; Nemati, Eghlim; Nathan, Viswam; Rahman, Mahbubur; Kuang, Jilong

doi:10.1109/embc44109.2020.9175597

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…In short, in the application scenario of nursing homes, the existing research cannot meet the needs of using audio for disease risk prediction, and the Risevi model we propose meets the current application needs of nursing homes. [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”

Section: Related Workmentioning

confidence: 99%

“…Disease Basic Algorithm Accuracy M. V. A. Rao [10] Asthma SVR 77.77% Y. You [17] Dementia KNN, SVM 97.2% S. Aich [19] Parkinson's disease linear classification 97.57% D. Pettas [20] Lung Disease LSTM 92.76% K. Sriskandaraja [26] Dementia KNN 91% M. T. Guimarães [27] Huntington's disease KNN 99% V. Ramesh [29] Cough GAN 76% Y. F. Khan [37] Alzheimer's Disease CNN, LSTM 85.05% S. Kamepalli [40] Cardiac LSTM 85%…”

Section: Researchermentioning

confidence: 99%

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Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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The intensification of population aging has brought pressure on public medical care. In order to reduce this pressure, we combined the image classification method with computer vision and used audio data that is easy to collect in nursing homes. Based on MelGAN, transfer learning, and Vision Transformer, we propose an application called Risevi (A Disease Risk Prediction Model Based on Vision Transformer), a disease risk prediction model for nursing homes. We first design a sample generation method based on MelGAN, then refer to the Mel frequency cepstral coefficient and the Wav2vec2 model to design the sample feature extraction method, perform floating-point operations on the tensor of the extracted features, and then convert it into a waveform. We then design a sample feature classification method based on transfer learning and Vision Transformer. Finally, we obtain the Risevi model. In this paper, we use public datasets and subject data as sample data. The experimental results show that the Risevi model has achieved an accuracy rate of 98.5%, a precision rate of 96.38%, a recall rate of 98.17%, and an F1 score of 97.15%. The experimental results show that the Risevi model can provide practical support for reducing public medical pressure.

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Section: Related Workmentioning

confidence: 99%

Section: Researchermentioning

confidence: 99%

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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“…In the studies conducted in References 122,123, deep learning‐based classification models and techniques that classify respiratory sounds based on mel‐spectrograms were developed to classify breathing sound anomalies (e.g., wheeze, crackle) for the automatic diagnosis of respiratory and pulmonary diseases, and these were proven to be efficient methods. In Reference 124, audio characteristics of coughs were used to build classifiers that can differentiate various respiratory disorders in adults.…”

Section: Acoustic Aimentioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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“…Synthetic data have driven forward innovations within the healthcare space. Synthetic data undergird many current initiatives in medical education [125,126], clinical training [127,128], epidemiology research [129,130] and disease prevention [131,132]. Cancer researchers now use synthetic data resources to bolster their work including precision medicine [133] and palliative care [134].…”

Section: Synthetic Datamentioning

confidence: 99%

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

McDonnell

2023

JCM

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Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

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CoughGAN: Generating Synthetic Coughs that Improve Respiratory Disease Classification

Cited by 11 publications

References 20 publications

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Risevi: A Disease Risk Prediction Model Based on Vision Transformer Applied to Nursing Homes

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

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