ItemArticle

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

Title
Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification
Creator
Hemanth, K.S.; Harisha Naik, T.; Kartik, N.; Kumar, N. Nanda; Senthilkumar, S.; Ramya, R.
Description
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. 2026, Hemanth K S, Harisha Naik T, N Kartik, N Nanda kumar, S Senthilkumar and Ramya R.
Source
Journal of Information Technology Management;Volume;18;Issue;1;pp.123-140
Date
01-01-2026
Publisher
University of Tehran
Subject
CNN; Deep Neural Network; Inverse Transform Sampling; Pre-trained Models; Respiratory Diseases
Coverage
Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, India; Harisha Naik T., Department of Computer Applications, Presidency College, Bangalore, India; Kartik N., Department of Commerce, Manipal Academy of Higher Education, Manipal, India; Kumar N.N., Department of Computer Science and Engineering, Excel Engineering College, Tamilnadu, Komarpalayampalyam, India; Senthilkumar S., Department of Computer Science and Engineering, Vinayaka Mission`s Kirupananda Variyar Engineering College, (Vinayaka Mission`s Research Foundation), Salem, India; Ramya R., Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamilnadu, Erode, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 20085893;
Format
online
Language
English
Type
Article

Citation

Hemanth, K.S.; Harisha Naik, T.; Kartik, N.; Kumar, N. Nanda; Senthilkumar, S.; Ramya, R., “Attentional Deep Learning with Inverse Transform Sampling for Robust Respiratory Sound Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23390.

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