Classification of Pulmonary Diseases Using Cough Sounds
Y. Venkata Lakshmi, Kotekanti Thanjeem, Thiruveedula Vishnu Vardhan, Bopella Prathyusha, Rajanna gari Sudarshan, Namaji Subhan, Gulipogula Vishnu
Pulmonary diseases include Asthma, Chronic Obstructive Pulmonary Disease (COPD), Pneumonia and
Bronchiolitis are the pose significant health risks worldwide. Early detection and accurate classification
of these diseases are critical for timely treatment and improved patient outcomes. Traditional diagnostic
methods such as imaging and lung function tests are often resource-intensive and time-consuming.
Alternatively, recent research has explored the use of non-invasive methods, such as analysing
cough sounds for pulmonary disease classification. Cough sounds contain valuable information that
reflects the underlying respiratory condition and approaches for disease detection. Existing system
contains limited dataset, influence of background noise and variability in cough sounds. The proposed
framework consists of three main stages: pre-processing, feature extraction, and classification. Using
the deep learning model Convolutional Neural Network (CNN) for extracting features and predicting
the respiratory disease based on the cough sound. Future work will focus on expanding the dataset,
improving feature extraction methods, and incorporating real-time analysis to develop a practical
diagnostic tool for clinical use.
