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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ullal, Babitha Sudhakar
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Computer Science & IT Engineering

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ResNet based deep learning approach for chronic obstructive pulmonary disease prediction using lung sound analysis Ullal, Babitha Sudhakar; Narasimhaiah, Veena Kalludi; Kamesh, Rithul
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1733-1745

Abstract

Chronic obstructive pulmonary disease (COPD) affects around 300-400 million people worldwide representing a critical healthcare challenge that requires early detection for effective intervention. This work introduces chronic lung analysis via audio signal prediction (CLASP), a novel framework achieving 97.90% accuracy in predicting COPD automatically through respiratory audio signal analysis. This method integrates advanced signal processing and deep learning architectures, comparing long short-term memory (LSTM), convolutional neural networks (CNN), and residual networks (ResNet) models for optimal performance. The ResNet architecture exhibits superior diagnostic capability with precision of 98.72%, recall of 96.86%, and 0.9937 area under the curve (AUC), as compared to existing methods by significant margins. These results establish a new benchmark for noninvasive COPD detection, thus enabling practical deployment in clinical settings thereby dramatically improving the patient outcomes by early detection and also reduce healthcare costs.
Co-Authors Kamesh, Rithul Narasimhaiah, Veena Kalludi