GUI Toolkit for Pulmonologists: Age-Gender Specific Lung Sound Analysis and Disease Identification Using Sequence Modeling

The objective diagnosis of respiratory diseases, by observing pathological sounds is a promising area of research in respiratory health. A GUI-based toolkit to aid a pulmonologist in decision-making is presented in this paper. Age and Gender-specificmachine learning models are trained and analyzed i...

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Published in2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS) Vol. 1; pp. 1025 - 1032
Main Authors Amose, John, P, Manimegalai, S, Pavithra, B, Susmitha, S, Ruth, S, Priyanga
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.03.2024
Subjects
Accuracy
Analytical models
Computational modeling
COPD
GRU
Long short term memory
LSTM
Lung
lung sound
MATLAB
Pneumonia
Respiratory diseases
Spectrogram
spectrograms
SVM
time-domain
Transformers
Visualization
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Summary:The objective diagnosis of respiratory diseases, by observing pathological sounds is a promising area of research in respiratory health. A GUI-based toolkit to aid a pulmonologist in decision-making is presented in this paper. Age and Gender-specificmachine learning models are trained and analyzed in this work for the classification of wheeze and crackle. Sequence models like LSTM, GRU, and Transformer were implemented, demonstrating high accuracy in classifying intricate lung sound features for diagnosing various respiratory diseases. In this work, MATLAB is used to enable user-friendly visualization, featuring filtered spectrograms and time- domain lung sound signals. In experimental evaluations, age and gender-specific models (child, adult, female) outperformed general models in the classification of wheeze and crackle. GRU and LSTM models achieved 69% accuracy, while the Transformer model outperformed with 750%.
ISBN:9798350384352
ISSN:2469-5556
DOI:10.1109/ICACCS60874.2024.10716908