Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification

Document Type : Research Paper

Authors

1 Department of Computer Science, Christ University, Bengaluru, India.

2 Department of Computer Applications, Presidency College, Bangalore, India.

3 Department of Commerce, Manipal Academy of Higher Education, Manipal, India.

4 Assistant Professor, Department of Computer Science and Engineering, Excel Engineering College Komarpalayampalyam, Tamilnadu, India.

5 Associate Professor, Department of Computer Science and Engineering, Vinayaka Mission`s Kirupananda Variyar Engineering College, Salem (Vinayaka Mission`s Research Foundation), India.

6 Associate professor, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, Tamilnadu, India.

10.22059/jitm.2026.106257

Abstract

The necessity for efficient breathing sound classification systems originates from respiratory diseases, which impair oxygen-carbon dioxide exchange and impact lung function. Feature extraction and pattern categorization are general components of such systems. Because of their effectiveness with big datasets, deep neural networks have acquired popularity recently in the category of breathing sounds. Enhancing medical care requires cooperation amongst researchers, medical professionals, and patients. An attentional deep learning model with inverse transform sampling is presented in this study to classify respiratory diseases from audio data. Robust models were developed to classify and detect respiratory elements using the Respiratory Sound dataset. The primary objectives include effectively determining lung sounds and determining respiratory illnesses. The architectures of CNN, VGG16, and ResNet50 were developed to extract features and categorize data. Also, the pre-trained models ResNet50 and VGG16 identify critical characteristics in spectrum pictures more accurately. Inverse transfer sampling is used to rectify class imbalance in respiratory datasets.  The models achieved 98% accuracy with the CNN model, 83% accuracy with VGG16, and 95% accuracy with ResNet50. Moreover, LSTM and CRNN models offer more information on how respiratory illnesses are classified.

Keywords

References

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Journal of Information Technology Management
Volume 18, Issue 1
2026
Pages 123-140

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