• Mallisetty Siva Mahesh
• Kattamuri B N Ayyappa
• Maddela Murali
• Mididoddi Surendra Babu
• Nagababu Pachhala

The goal of our experiment is to investigate if deep learning can identify pulmonary conditions from electronically recorded lung sounds. While support vector machines and K-Nearest Neighbors algorithms are examples of machine learning techniques, they are not very accurate in diagnosing pulmonary disorders. Thus, we presented a deep neural network model that identifies the respiratory system's state based on breathing sound input. A number of performance assessment criteria, such as Cohen's kappa, accuracy, sensitivity, specificity, precision, and F1-score, were used to assess the model's training. By fusing CNN and GRU, the proposed algorithm was able to categorize patients based on the categories of pulmonary diseases with the highest average accuracy and precision. A total of 103 patients from locally recorded stethoscope lung sounds at Abdullah University Hospital, Jordan University of Science and Technology, Jordan, were included in the chosen data-set. use of deep neural networks or deep learning to predict respiratory conditions such as bronchiectasis, pneumonia, bronchiolitis, URTIs (upper respiratory tract infections), and COPD. This work opens the door for the application of deep learning models in clinical settings to support physicians in making diagnose-related decisions regarding pulmonary disorders.

Deep Learning, GRU, Respiratory Diseases, Classification

"DEEP LEARNING SOLUTIONS FOR RESPIRATORY DISORDERS THROUGH SOUND PATTERNS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.j145-j153, March-2024, Available :http://www.jetir.org/papers/JETIR2403924.pdf

"DEEP LEARNING SOLUTIONS FOR RESPIRATORY DISORDERS THROUGH SOUND PATTERNS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppj145-j153, March-2024, Available at : http://www.jetir.org/papers/JETIR2403924.pdf