Convolution-Vision Transformer for Automatic Lung Sound Classification

Auscultation is an essential part of clinical examination since it is an inexpensive, noninvasive, safe, and one of the oldest diagnostic techniques used to diagnose various pulmonary diseases. In literature, machine learning models were proposed in various studies for lung sound classification to o...

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Bibliographic Details
Published in2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) Vol. 1; pp. 97 - 102
Main Authors Neto, Jose, Arrais, Nicksson, Vinuto, Tiago, Lucena, Joao
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.10.2022
Subjects
Auscultation
Ear
Graphics
ICBHI dataset
Lung
Lung Sound Classification
Machine learning
Pulmonary diseases
Sensitivity
Vision Transformer
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Summary:Auscultation is an essential part of clinical examination since it is an inexpensive, noninvasive, safe, and one of the oldest diagnostic techniques used to diagnose various pulmonary diseases. In literature, machine learning models were proposed in various studies for lung sound classification to overcome the ear acuity and the inherent inter-listener variability. In this work, we propose a hybrid Convolution-Vision Transformer architecture that explores the usage of Convolutional with Vision Transformers in a single system. We evaluate our proposed method on ICBHI 2017 database for the four-class sound classification of lung sounds to demonstrate the effectiveness of our method which has achieved a score of 57.36% surpassing many state-of-art models.
ISSN:2377-5416
DOI:10.1109/SIBGRAPI55357.2022.9991756