This study investigates the application of respiratory audio signals for the detection of lung disorders, such as asthma, COPD, and pneumonia, utilizing convolutional neural networks (CNNs). Lung disorders frequently generate atypical noises such as wheezes, crackles, and stridor as a result of tracheal and pulmonary problems. Although manual inspection formerly enabled early detection, the intricacy of contemporary diseases demands more accurate diagnostic instruments. This research establishes a system that analyzes audio data, extracts features, and utilizes convolutional neural networks to categorize pulmonary disorders based on respiration sounds. Advanced methodologies such as digital stethoscopes and frequency modulation analysis are employed to segment and scrutinize lung sounds, facilitating the identification of respiratory abnormalities. The technology utilizes machine learning and AI to provide a non-invasive, economical method for identifying conditions such as pneumonia and interstitial lung disease. Despite limitations including hearing interference and data harmonization, this technology possesses significant potential to revolutionize early detection and remote monitoring of lung disorders, enhancing healthcare outcomes in both clinical and resource-limited environments.
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