Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method

• URL: http://arxiv.org/abs/2407.10828v1
• Date: Mon, 15 Jul 2024 15:40:02 GMT
• Title: Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method
• Authors: Yi-Wei Chua, Yun-Chien Cheng,
• Abstract summary: This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds. Addressing the issue of class imbalance and lack of diversity in existing respiratory sound datasets, our study employs a lightweight and highly accurate model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study aims to develop an auxiliary diagnostic system for classifying abnormal lung respiratory sounds, enhancing the accuracy of automatic abnormal breath sound classification through an innovative multi-label learning approach and multi-head attention mechanism. Addressing the issue of class imbalance and lack of diversity in existing respiratory sound datasets, our study employs a lightweight and highly accurate model, using a two-dimensional label set to represent multiple respiratory sound characteristics. Our method achieved a 59.2% ICBHI score in the four-category task on the ICBHI2017 dataset, demonstrating its advantages in terms of lightweight and high accuracy. This study not only improves the accuracy of automatic diagnosis of lung respiratory sound abnormalities but also opens new possibilities for clinical applications.

Related papers

• Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification [1.690115983364313]
  We introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.
  arXiv  Detail & Related papers  (2023-12-15T08:34:31Z)
• Respiratory Disease Classification and Biometric Analysis Using Biosignals from Digital Stethoscopes [3.2458203725405976]
  This work presents a novel approach leveraging digital stethoscope technology for automatic respiratory disease classification and biometric analysis. By leveraging one of the largest publicly available medical database of respiratory sounds, we train machine learning models to classify various respiratory health conditions. Our approach achieves high accuracy in both binary classification (89% balanced accuracy for healthy vs. diseased) and multi-class classification (72% balanced accuracy for specific diseases like pneumonia and COPD)
  arXiv  Detail & Related papers  (2023-09-12T23:54:00Z)
• 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)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis [58.720142291102135]
  The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The use of information about outlier and Fractal Dimension features improves the system performance.
  arXiv  Detail & Related papers  (2022-03-21T09:57:20Z)
• 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)
• The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds [2.294014185517203]
  The statistical features are calculated from intrinsic mode functions that are extracted by applying the Hilbert Transform to the lung sounds. The classification of the lung sounds from asthma and healthy subjects is performed using Deep Belief Networks (DBN)
  arXiv  Detail & Related papers  (2021-01-20T19:04:33Z)
• Respiratory Sound Classification Using Long-Short Term Memory [62.997667081978825]
  This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. An examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.
  arXiv  Detail & Related papers  (2020-08-06T23:11:57Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.