Commonly known causes of COPD include tobacco smoking, genetic disorder (alpha-1-antitrypsin deficiency), air pollution, etc. ![]() Bronchitis leads to inflamed and narrowed airways ( Khatri & Tamil, 2018). These two conditions usually occur together and can vary in severity among COPD individuals. Emphysema and chronic bronchitis are the two crucial conditions contributing to COPD ( Weese & Lorenz, 2016). The lungs are unable to get enough oxygen and give out unwanted carbon dioxide. Chronic Obstructive Pulmonary Disease (COPD) is a disease name used as an umbrella of a broad group of lung diseases that block the airflow in and out of the lungs due to narrow air passages, making it difficult to breathe. Not too long ago, we relied purely on human intelligence, ability and skillset to interpret and understand the enormous amount of medical data generated by various equipment and machinery types. They may be subjected to extensive variations due to a different analysis by different interpreters/doctors ( Rocha et al., 2019). Various medical equipment's images and audio could have multiple limitations due to their subjectivity, clarity, and complexity. It is one of the most vital and essential sectors where people expect the highest levels of diagnosis, treatment, and high quality of services in accordance with the money they spend. The healthcare sector is an independent and one of the most critical sectors compared to various other industries. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. The investigation results validate the success of the proposed deep learning approach. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. ![]() The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. ![]() Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease. Cite this article Srivastava A, Jain S, Miranda R, Patil S, Pandya S, Kotecha K. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. 2 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharastra, India DOI 10.7717/peerj-cs.369 Published Accepted Received Academic Editor Marcin Woźniak Subject Areas Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech Keywords Deep learning, CNN based classification, Medical-assistive technology, Respiratory sound analysis, Machine learning Copyright © 2021 Srivastava et al.
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