International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Lung Sound Classification for Respiratory Disease by using CNN-LSTM |
|---|---|
| ABSTRACT | The project “Lung Sound Classification for Respiratory Disease Using CNN-LSTM” aims to assist in the early detection and classification of respiratory diseases through automated analysis of lung sound signals. Respiratory disorders such as asthma, pneumonia, and bronchitis often exhibit distinct acoustic patterns that are difficult to identify accurately through manual examination alone. The proposed system processes recorded lung sound data by applying audio preprocessing, visualization, and feature extraction techniques to enhance signal quality and represent important acoustic characteristics. A hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is employed to capture both spatial features and temporal dependencies present in lung sounds. CNN layers extract meaningful patterns from spectrogram representations, while LSTM layers model the sequential nature of respiratory signals for improved classification performance. By leveraging the CNN-LSTM architecture, the system aims to provide reliable and efficient classification of respiratory diseases, supporting healthcare professionals in diagnosis and reducing dependency on subjective interpretation. This approach contributes toward improved accuracy, early intervention, and better clinical decision support in respiratory healthcare. |
| AUTHOR | BAKKIYA LAKSHMI, SRINIVAS R, CHARAN SAGAR K, JAGADEESH H B, PAVAN V NAIK Assistant Professor, Department of Computer Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka, India UG Students, Department of Computer Science and Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401036 |
| pdf/36_Lung Sound Classification for Respiratory Disease by using CNN-LSTM.pdf | |
| KEYWORDS | |
| References | [1] T. Nguyen et al., “Lung sound classification using co-tuning and stochastic normalization”, 2021. [2] Y. Ma et al., “Lung RN+NL: An improved adventitious lung sound classification using non-local block ResNet neural network with mixup data augmentation”, 2020. [3] T. Nguyen et al., “Lung sound classification using snapshot ensemble of convolutional neural networks”, 2020. [4] J. Watt, R. Borhani, and A. K. Katsaggelos, “Machine learning refined: Foundations, algorithms, and applications”, 2020. [5] B. M. Rocha et al., “An open access database for the evaluation of respiratory sound classification algorithms”, 2019. [6] U. R. Acharya et al., “Deep convolutional neural network for the automated diagnosis of lung diseases using lung sounds”, 2019. [7] D. Perna and A. Tagarelli, “Deep auscultation: Predicting respiratory anomalies and diseases via recurrent neural networks”, 2019. [8] F. Demir et al., “Classification of lung sounds with CNN model using parallel pooling structure”, 2020. [9] M. Pahar et al., “Automatic classification of respiratory diseases using lung sound analysis”, 2019. [10] H. Chen et al., “Automated respiratory sound analysis for classification of pulmonary diseases using CNN-LSTM”, 2020. |
