Deep learning models improve asthma/COPD diagnosis from lung sounds

By PulseAugur Editorial · [3 sources] · 2026-06-09 15:12

Researchers have developed deep learning models, specifically CNNs and GRUs, to differentiate between asthma and COPD using pulmonary sound data. The study optimized input representations like MFCC matrices and log-mel spectrograms, finding MFCCs to be superior. Adaptive-length windowing was crucial for handling inconsistent temporal dimensions in spectrograms, leading to the best cycle-based F1-score of 0.877 and subject-based F1-score of 0.855. AI

IMPACT Novel deep learning approaches show promise for more accurate differential diagnosis of respiratory conditions using audio data.

RANK_REASON Academic paper detailing novel methods for medical diagnosis using deep learning.

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AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

arXiv cs.AI TIER_1 English(EN) · Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez · 2026-06-10 04:00

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

arXiv:2606.10972v1 Announce Type: cross Abstract: This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based r…
arXiv cs.AI TIER_1 English(EN) · Elena Battini Sonmez · 2026-06-09 15:12

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based representations suffer from inconsistent temporal d…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-09 15:12

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

This study aims to explore the performance of the VAR model in comparison with mel-frequency cepstral coefficient (MFCC) matrices and log-mel spectrograms using deep learning. In pulmonary sound classification, spectrogram-based representations suffer from inconsistent temporal d…

COVERAGE [3]

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks

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