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Deep learning models improve asthma/COPD diagnosis from lung sounds

By PulseAugur Editorial · [3 sources] ·

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.

Read on arXiv cs.AI →

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

Deep learning models improve asthma/COPD diagnosis from lung sounds

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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Ipek Sen, Ozgur Ozdemir, Elena Battini Sonmez ·

    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…

  2. arXiv cs.AI TIER_1 English(EN) · Elena Battini Sonmez ·

    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…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    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…

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