Developing a Multi-variate Prediction Model For COVID-19 From
Crowd-sourced Respiratory Voice Data
- URL: http://arxiv.org/abs/2402.07619v1
- Date: Mon, 12 Feb 2024 12:52:47 GMT
- Title: Developing a Multi-variate Prediction Model For COVID-19 From
Crowd-sourced Respiratory Voice Data
- Authors: Yuyang Yan, Wafaa Aljbawi, Sami O. Simons, Visara Urovi
- Abstract summary: We develop a deep learning model to identify COVID-19 from voice recording data.
We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4352 participants via a COVID-19 Sounds app.
Based on the voice data, we develop deep learning classification models to detect COVID-19 cases.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: COVID-19 has affected more than 223 countries worldwide and in the Post-COVID
Era, there is a pressing need for non-invasive, low-cost, and highly scalable
solutions to detect COVID-19. We develop a deep learning model to identify
COVID-19 from voice recording data. The novelty of this work is in the
development of deep learning models for COVID-19 identification from only voice
recordings. We use the Cambridge COVID-19 Sound database which contains 893
speech samples, crowd-sourced from 4352 participants via a COVID-19 Sounds app.
Voice features including Mel-spectrograms and Mel-frequency cepstral
coefficients (MFCC) and CNN Encoder features are extracted. Based on the voice
data, we develop deep learning classification models to detect COVID-19 cases.
These models include Long Short-Term Memory (LSTM) and Convolutional Neural
Network (CNN) and Hidden-Unit BERT (HuBERT). We compare their predictive power
to baseline machine learning models. HuBERT achieves the highest accuracy of
86\% and the highest AUC of 0.93. The results achieved with the proposed models
suggest promising results in COVID-19 diagnosis from voice recordings when
compared to the results obtained from the state-of-the-art.
Related papers
- Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas.
This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
arXiv Detail & Related papers (2024-09-29T07:04:26Z) - EDAC: Efficient Deployment of Audio Classification Models For COVID-19
Detection [0.0]
The global spread of COVID-19 had severe consequences for public health and the world economy.
Various researchers made use of machine learning methods in an attempt to detect COVID-19.
The solutions leverage various input features, such as CT scans or cough audio signals, with state-of-the-art results arising from deep neural network architectures.
To address this, we first recreated two models that use cough audio recordings to detect COVID-19.
arXiv Detail & Related papers (2023-09-11T10:07:51Z) - COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for
COVID-19 Symptom Prediction and Recommendation [75.74756992992147]
We introduce the design of COVID-Net Assistant, an efficient virtual assistant designed to provide symptom prediction and recommendations for COVID-19.
We explore a variety of highly customized, lightweight convolutional neural network architectures generated via machine-driven design exploration.
Our experimental results show promising, with the COVID-Net Assistant neural networks demonstrating robust predictive performance.
arXiv Detail & Related papers (2022-11-22T01:41:48Z) - Developing a multi-variate prediction model for the detection of
COVID-19 from Crowd-sourced Respiratory Voice Data [0.0]
The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings.
We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app.
Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases.
arXiv Detail & Related papers (2022-09-08T11:46:37Z) - On the pragmatism of using binary classifiers over data intensive neural
network classifiers for detection of COVID-19 from voice [34.553128768223615]
We show that detecting COVID-19 from voice does not require custom-made non-standard features or complicated neural network classifiers.
We demonstrate this from a human-curated dataset collected and calibrated in clinical settings.
arXiv Detail & Related papers (2022-04-11T00:19:14Z) - Project Achoo: A Practical Model and Application for COVID-19 Detection
from Recordings of Breath, Voice, and Cough [55.45063681652457]
We propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices.
The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification.
We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection.
arXiv Detail & Related papers (2021-07-12T08:07:56Z) - Virufy: A Multi-Branch Deep Learning Network for Automated Detection of
COVID-19 [1.9899603776429056]
Researchers have successfully presented models for detecting COVID-19 infection status using audio samples recorded in clinical settings.
We propose a multi-branch deep learning network that is trained and tested on crowdsourced data where most of the data has not been manually processed and cleaned.
arXiv Detail & Related papers (2021-03-02T15:31:09Z) - Automated Model Design and Benchmarking of 3D Deep Learning Models for
COVID-19 Detection with Chest CT Scans [72.04652116817238]
We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification.
We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
arXiv Detail & Related papers (2021-01-14T03:45:01Z) - End-2-End COVID-19 Detection from Breath & Cough Audio [68.41471917650571]
We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples.
We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation.
arXiv Detail & Related papers (2021-01-07T01:13:00Z) - Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural
Networks [68.8204255655161]
We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not.
Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
arXiv Detail & Related papers (2020-12-29T01:14:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.