arXiv 2201.030542022-01-09cs.SDeess.AS

An Ensemble of Deep Learning Frameworks Applied For Predicting Respiratory Anomalies

Center for Digital Safety & Security, Austria Institute of Technology（奥地利技术研究所数字安全与安保中心）；School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；FPT Software Company Limited（FPT软件有限公司）；Singapore Institute of Technology（新加坡理工学院）

Lam Pham, Dat Ngo, Truong Hoang, Alexander Schindler, Ian McLoughlin

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

In this paper, we evaluate various deep learning frameworks for detecting respiratory anomalies from input audio recordings. To this end, we firstly transform audio respiratory cycles collected from patients into spectrograms where both temporal and spectral features are presented, referred to as the front-end feature extraction. We then feed the spectrograms into back-end deep learning networks for classifying these respiratory cycles into certain categories. Finally, results from high-performed deep learning frameworks are fused to obtain the best score. Our experiments on ICBHI benchmark dataset achieve the highest ICBHI score of 57.3 from a late fusion of inception based and transfer learning based deep learning frameworks, which outperforms the state-of-the-art systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

一种用于检测呼吸异常的具有时空聚焦的深度学习架构

A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies

School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Center for Digital Safety & Security, Austrian Institute of Technology（奥地利技术研究所数字安全与安保中心）；Amazon Alexa（亚马逊Alexa）；Nuffield Department of Clinical Neurosciences, University of Oxford（牛津大学纳菲尔德临床神经科学系）

本文提出一种基于连续小波变换和Inception-residual骨干的深度学习架构，结合时空聚焦与多头注意力机制检测呼吸异常，在SPRSound基准上取得Top-1性能。

2023-06-25cs.SDeess.AS

Inception-Based Network and Multi-Spectrogram Ensemble Applied For Predicting Respiratory Anomalies and Lung Diseases

Austria Institute of Technology（奥地利技术研究所）；Queen Mary University of London（伦敦玛丽女王大学）；University of Lübeck（吕贝克大学）；Singapore Institute of Technology（新加坡理工学院）

This paper presents an inception-based deep neural network for detecting lung diseases using respiratory sound input. Recordings of respiratory sound collected from patients are firstly transformed into spectrograms where both spectral and temporal information are well presented, referred to as front-end feature extraction. These spectrograms are then fed into the proposed network, referred to as back-end classification, for detecting whether patients suffer from lung-relevant diseases. Our experiments, conducted over the ICBHI benchmark meta-dataset of respiratory sound, achieve competitive I…

2020-12-26cs.LGcs.SDeess.AS

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases

University of Kent（肯特大学）；University of Science and Technology of China（中国科学技术大学）；University of Oxford（牛津大学）；Graz University of Technology（格拉茨理工大学）

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this pa…

2020-01-21cs.LGcs.SDeess.AS

Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks

University of Calabria（卡拉布里亚大学）

Respiratory diseases are among the most common causes of severe illness and death worldwide. Prevention and early diagnosis are essential to limit or even reverse the trend that characterizes the diffusion of such diseases. In this regard, the development of advanced computational tools for the analysis of respiratory auscultation sounds can become a game changer for detecting disease-related anomalies, or diseases themselves. In this work, we propose a novel learning framework for respiratory auscultation sound data. Our approach combines state-of-the-art feature extraction techniques and adv…

2019-07-11cs.LGcs.SDeess.AS

CNN-MoE based framework for classification of respiratory anomalies and lung disease detection

University of Kent（肯特大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Lübeck（吕贝克大学）；Singapore Institute of Technology（新加坡理工学院）

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect disease, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory-sound…

2020-06-02cs.LGcs.SDeess.AS

一种基于Inception-Residual架构与多目标损失的呼吸异常检测方法

An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies

Queen Mary University of London（伦敦玛丽女王大学）；University of Essex（埃塞克斯大学）；Austrian Institute of Technology（奥地利技术研究所）；Amazon Alexa（亚马逊Alexa）；University of Oxford（牛津大学）

本文提出一种基于Inception-Residual架构、多头注意力和多目标损失的深度学习系统，通过线性组合多频谱图特征，在SPRSound基准上显著提升呼吸异常检测性能，并在两项任务中取得最优得分。

2023-06-19cs.LGcs.SDeess.AS

更多相关论文收起更多论文

Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds

Ho Chi Minh City University of Technology（胡志明市理工大学）；Da Nang University of Science and Technology（岘港科技大学）

This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C- DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a res…

2020-12-26cs.CVcs.LGcs.SD

Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning

Nanyang Technological University（南洋理工大学）

The primary objective of this paper is to build classification models and strategies to identify breathing sound anomalies (wheeze, crackle) for automated diagnosis of respiratory and pulmonary diseases. In this work we propose a deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms. We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection. Moreover, we devise a local log quantization strategy for model weights to r…

2020-04-16cs.LGcs.SDeess.AS