Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds

• URL: http://arxiv.org/abs/2106.12174v1
• Date: Wed, 23 Jun 2021 05:49:20 GMT
• Title: Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds
• Authors: Balamurali B T, Hwan Ing Hee, Saumitra Kapoor, Oon Hoe Teoh, Sung Shin Teng, Khai Pin Lee, Dorien Herremans, Jer Ming Chen
• Abstract summary: We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs. We collected a new dataset of cough sounds, labelled with clinician's diagnosis.
• Score: 6.376404422444008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician's diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs -- healthy or pathology (in general or belonging to a specific respiratory pathology), reaches accuracy exceeding 84\% when classifying cough to the label provided by the physicians' diagnosis. In order to classify subject's respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91\% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among the four classes of coughs, overall accuracy dropped: one class of pathological coughs are often misclassified as other. However, if one consider the healthy cough classified as healthy and pathological cough classified to have some kind of pathologies, then the overall accuracy of four class model is above 84\%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological cough irrespective of the underlying conditions occupy the same feature space making it harder to differentiate only using MFCC features.

Related papers

• Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data [0.3749861135832073]
  This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. A Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.
  arXiv  Detail & Related papers  (2024-02-03T15:17:32Z)
• Federated Few-shot Learning for Cough Classification with Edge Devices [6.141439238018306]
  This work aims to develop a framework that can perform cough classification even in situations when enormous cough data is not available. We adopt few-shot learning and federated learning to design a novel framework, termed F2LCough, for solving the newly formulated problem. Our results show the feasibility of few-shot learning combined with federated learning to build a classification model of cough sounds.
  arXiv  Detail & Related papers  (2023-09-03T04:48:41Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays Image [11.076902397190961]
  This study explores the effectiveness of established neural network architectures on the pneumonia classification task through the transfer learning paradigm. To present a comprehensive comparison, 12 well-known ImageNet pre-trained models were fine-tuned and used to discriminate among chest-x-rays of healthy people. The experiments were performed using a total of 6330 images split between train, validation and test sets.
  arXiv  Detail & Related papers  (2021-10-06T14:00:18Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• 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)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Can Machine Learning Be Used to Recognize and Diagnose Coughs? [3.2265234594751155]
  We present a low complexity, automated recognition and diagnostic tool for screening respiratory infections. We use Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient.
  arXiv  Detail & Related papers  (2020-04-01T20:14:36Z)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)
• Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases [26.786743524562322]
  This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. 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.
  arXiv  Detail & Related papers  (2020-01-21T15:26:52Z)

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.