# A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram

arXiv: https://arxiv.org/abs/2009.04402 · HTML: https://mlsanity.com/paper/2009.04402/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 13 of 13)

- [A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity](https://mlsanity.com/paper/2306.14169/) (2023)
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- [Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings](https://mlsanity.com/paper/2106.15835/) (2021)
- [AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformers](https://mlsanity.com/paper/2305.00046/) (2023)
- [Federated Few-shot Learning for Cough Classification with Edge Devices](https://mlsanity.com/paper/2309.01076/) (2023)
- [An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification](https://mlsanity.com/paper/2204.09909/) (2022)
- [Exploring traditional machine learning for identification of pathological auscultations](https://mlsanity.com/paper/2209.00672/) (2022)
- [CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning](https://mlsanity.com/paper/2502.00734/) (2025)
- [AI- Enhanced Stethoscope in Remote Diagnostics for Cardiopulmonary Diseases](https://mlsanity.com/paper/2505.18184/) (2025)
- [A Multi-Stage Hybrid CNN-Transformer Network for Automated Pediatric Lung Sound Classification](https://mlsanity.com/paper/2507.20408/) (2025)
- [EZhouNet:A framework based on graph neural network and anchor interval for the respiratory sound event detection](https://mlsanity.com/paper/2509.01153/) (2025)
- [Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis](https://mlsanity.com/paper/2604.22695/) (2026)

## Abstract (arXiv)

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases using hybrid scalogram-based features of lung sounds. The hybrid scalogram features utilize the empirical mode decomposition (EMD) and continuous wavelet transform (CWT). The proposed scheme's performance is studied using a patient independent train-validation set from the publicly available ICBHI 2017 lung sound dataset. Employing the proposed framework, weighted accuracy scores of 99.20% for ternary chronic classification and 99.05% for six-class pathological classification are achieved, which outperform well-known and much larger VGG16 in terms of accuracy by 0.52% and 1.77% respectively. The proposed CNN model also outperforms other contemporary lightweight models while being computationally comparable.