COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram
Transformers
- URL: http://arxiv.org/abs/2207.09529v2
- Date: Sat, 27 May 2023 00:25:56 GMT
- Title: COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram
Transformers
- Authors: Idil Aytekin, Onat Dalmaz, Kaan Gonc, Haydar Ankishan, Emine U
Saritas, Ulas Bagci, Haydar Celik and Tolga Cukur
- Abstract summary: We introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds.
The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds.
HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context.
- Score: 1.4091863292043447
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Monitoring of prevalent airborne diseases such as COVID-19 characteristically
involves respiratory assessments. While auscultation is a mainstream method for
preliminary screening of disease symptoms, its utility is hampered by the need
for dedicated hospital visits. Remote monitoring based on recordings of
respiratory sounds on portable devices is a promising alternative, which can
assist in early assessment of COVID-19 that primarily affects the lower
respiratory tract. In this study, we introduce a novel deep learning approach
to distinguish patients with COVID-19 from healthy controls given audio
recordings of cough or breathing sounds. The proposed approach leverages a
novel hierarchical spectrogram transformer (HST) on spectrogram representations
of respiratory sounds. HST embodies self-attention mechanisms over local
windows in spectrograms, and window size is progressively grown over model
stages to capture local to global context. HST is compared against
state-of-the-art conventional and deep-learning baselines. Demonstrations on
crowd-sourced multi-national datasets indicate that HST outperforms competing
methods, achieving over 83% area under the receiver operating characteristic
curve (AUC) in detecting COVID-19 cases.
Related papers
- COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals [0.6963971634605796]
This research aims to explore various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals.
It investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, when applied to two machine learning algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP)
The proposed system provides a practical solution and demonstrates state-of-the-art classification performance, with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy
arXiv Detail & Related papers (2023-09-08T08:33:24Z) - Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on
Respiratory Sound Classification [19.180927437627282]
We introduce a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space.
Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.
arXiv Detail & Related papers (2023-05-23T13:04:07Z) - Fused Audio Instance and Representation for Respiratory Disease
Detection [0.6827423171182154]
We propose Fused Audio Instance and Representation (FAIR) as a method for respiratory disease detection.
We conducted experiments on the use case of COVID-19 detection by combining waveform and spectrogram representation of body sounds.
arXiv Detail & Related papers (2022-04-22T09:01:29Z) - Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based
Sparse PCA Network [93.22587316229954]
We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E)
We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
arXiv Detail & Related papers (2021-10-27T19:28:36Z) - EIHW-MTG DiCOVA 2021 Challenge System Report [2.3544007354006706]
This paper aims to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs.
We focus on analysing the spectrogram representations of coughing samples with the aim to investigate whether COVID-19 alters the frequency content of these signals.
arXiv Detail & Related papers (2021-10-13T07:38:54Z) - 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) - CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19
Patients Using Deep Learning [133.87426554801252]
We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans.
Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
arXiv Detail & Related papers (2021-05-25T12:06:55Z) - Exploring Self-Supervised Representation Ensembles for COVID-19 Cough
Classification [5.469841541565308]
We propose a novel self-supervised learning enabled framework for COVID-19 cough classification.
A contrastive pre-training phase is introduced to train a Transformer-based feature encoder with unlabelled data.
We show that the proposed contrastive pre-training, the random masking mechanism, and the ensemble architecture contribute to improving cough classification performance.
arXiv Detail & Related papers (2021-05-17T01:27:20Z) - Quantification of pulmonary involvement in COVID-19 pneumonia by means
of a cascade oftwo U-nets: training and assessment on multipledatasets using
different annotation criteria [83.83783947027392]
This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions.
We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets.
The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
arXiv Detail & Related papers (2021-05-06T10:21:28Z) - 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) - Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.
Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
arXiv Detail & Related papers (2020-05-24T23:13:16Z)
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.