Dec 15, 2021 · This study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones with only a small labeled ...
Dec 31, 2020 · This study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones with only a small labeled ...
Based on a four-layers CNN, this study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones ...
This paper uses cross pseudo supervision to leverage a small amount of labeled audio data and a larger amount of unlabeled data to perform automated ...
Based on a four-layers CNN, this study proposes graph semi-supervised CNNs (GS-CNNs), which can classify respiratory sounds into normal, crackle and wheeze ones ...
Oct 22, 2024 · The analysis of lung sounds, collected through auscultation, is a fundamental component of pulmonary disease diagnostics for primary care ...
This paper presents the development of a semi-supervised deep learning algorithm for automatically classify lung sounds from a relatively large number of ...
Sep 11, 2017 · In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio ...
Nov 17, 2021 · In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial ...
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This review explores the latest advances in artificial intelligence (AI) and machine learning (ML) for the identification and classification of lung sounds.