ArticleBiomedical signal processing and control2023
Interpretation of lung disease classification with light attention connected module.
Article in Biomedical signal processing and control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- A Novel AAF-SwinT Model for Automatic Recognition of Abnormal Goat Lung Sounds.Animals : an open access journal from MDPI · 2026Article
- Non-contact lung disease classification via orthogonal frequency division multiplexing-based passive 6G integrated sensing and communication.Communications medicine · 2026Article
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Fused Audio Instance and Representation for Respiratory Disease Detection.Sensors (Basel, Switzerland) · 2024Article
- Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning-based lung sound analysis for intelligent stethoscope.Military Medical Research · 2023Review
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.
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