Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm

Nayan Kanti Majumdar; Sharon Christa; Asha S. Manek; Shelda Sajeev

doi:10.1109/ICWITE57052.2022.10176205

Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm

Nayan Kanti Majumdar, Sharon Christa, Asha S. Manek, Shelda Sajeev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

It is crucial to improve access to quality healthcare for the economically underprivileged community in order to ensure that imperative illnesses may be treated quickly. In situations where medical personnel are severely lacking, a simple categorization of respiratory tones using a computerized instrument can be performed to provide a rapid diagnosis for respiratory-related disorders such as Respiratory function. In this paper, it presents Respiratory Specification, an upgraded bi-ResNet deep learning architecture that employs STFT and wavelet extraction of features approaches to boost performance over previous work. The authorized data compared to the global and the "train-and-test" datasets splitting procedure from the ICBHI 2017 challenge to create a fair assessment. As an outcome, able to obtain a productivity of 51.17 percent, which would be the best prediction consequence among all ICBHI 2017 teams participating.

Original language	English
Title of host publication	2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE)
Subtitle of host publication	Proceedings
Place of Publication	United States
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-4
Number of pages	4
ISBN (Electronic)	978-1-6654-6490-1
ISBN (Print)	978-1-6654-6491-8
DOIs	https://doi.org/10.1109/ICWITE57052.2022.10176205
Publication status	Published - 14 Jul 2023
Externally published	Yes
Event	2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship - Bangalore, India Duration: 1 Dec 2022 → 3 Dec 2022

Conference

Conference	2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship
Abbreviated title	ICWITE 2022
Country/Territory	India
City	Bangalore
Period	1/12/22 → 3/12/22

Keywords

Deep Learning
Respiratory Sound Classification
STFT
Support Vector Method
Wavelet Evaluation

Access to Document

10.1109/ICWITE57052.2022.10176205Licence: In Copyright

Cite this

Majumdar, N. K., Christa, S., Manek, A. S., & Sajeev, S. (2023). Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm. In 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE) : Proceedings (pp. 1-4). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICWITE57052.2022.10176205

@inproceedings{bad5ee52c1d24f6c8c0f31db7d027df2,

title = "Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm",

abstract = "It is crucial to improve access to quality healthcare for the economically underprivileged community in order to ensure that imperative illnesses may be treated quickly. In situations where medical personnel are severely lacking, a simple categorization of respiratory tones using a computerized instrument can be performed to provide a rapid diagnosis for respiratory-related disorders such as Respiratory function. In this paper, it presents Respiratory Specification, an upgraded bi-ResNet deep learning architecture that employs STFT and wavelet extraction of features approaches to boost performance over previous work. The authorized data compared to the global and the {"}train-and-test{"} datasets splitting procedure from the ICBHI 2017 challenge to create a fair assessment. As an outcome, able to obtain a productivity of 51.17 percent, which would be the best prediction consequence among all ICBHI 2017 teams participating.",

keywords = "Deep Learning, Respiratory Sound Classification, STFT, Support Vector Method, Wavelet Evaluation",

author = "Majumdar, {Nayan Kanti} and Sharon Christa and Manek, {Asha S.} and Shelda Sajeev",

year = "2023",

month = jul,

day = "14",

doi = "10.1109/ICWITE57052.2022.10176205",

language = "English",

isbn = "978-1-6654-6491-8",

pages = "1--4",

booktitle = "2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2022 ; Conference date: 01-12-2022 Through 03-12-2022",

}

Majumdar, NK, Christa, S, Manek, AS & Sajeev, S 2023, Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm. in 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE) : Proceedings. Institute of Electrical and Electronics Engineers, United States, pp. 1-4, 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, Bangalore, India, 1/12/22. https://doi.org/10.1109/ICWITE57052.2022.10176205

Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm. / Majumdar, Nayan Kanti; Christa, Sharon; Manek, Asha S. et al.
2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE) : Proceedings. United States: Institute of Electrical and Electronics Engineers, 2023. p. 1-4.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm

AU - Majumdar, Nayan Kanti

AU - Christa, Sharon

AU - Manek, Asha S.

AU - Sajeev, Shelda

PY - 2023/7/14

Y1 - 2023/7/14

N2 - It is crucial to improve access to quality healthcare for the economically underprivileged community in order to ensure that imperative illnesses may be treated quickly. In situations where medical personnel are severely lacking, a simple categorization of respiratory tones using a computerized instrument can be performed to provide a rapid diagnosis for respiratory-related disorders such as Respiratory function. In this paper, it presents Respiratory Specification, an upgraded bi-ResNet deep learning architecture that employs STFT and wavelet extraction of features approaches to boost performance over previous work. The authorized data compared to the global and the "train-and-test" datasets splitting procedure from the ICBHI 2017 challenge to create a fair assessment. As an outcome, able to obtain a productivity of 51.17 percent, which would be the best prediction consequence among all ICBHI 2017 teams participating.

AB - It is crucial to improve access to quality healthcare for the economically underprivileged community in order to ensure that imperative illnesses may be treated quickly. In situations where medical personnel are severely lacking, a simple categorization of respiratory tones using a computerized instrument can be performed to provide a rapid diagnosis for respiratory-related disorders such as Respiratory function. In this paper, it presents Respiratory Specification, an upgraded bi-ResNet deep learning architecture that employs STFT and wavelet extraction of features approaches to boost performance over previous work. The authorized data compared to the global and the "train-and-test" datasets splitting procedure from the ICBHI 2017 challenge to create a fair assessment. As an outcome, able to obtain a productivity of 51.17 percent, which would be the best prediction consequence among all ICBHI 2017 teams participating.

KW - Deep Learning

KW - Respiratory Sound Classification

KW - STFT

KW - Support Vector Method

KW - Wavelet Evaluation

UR - http://www.scopus.com/inward/record.url?scp=85166466126&partnerID=8YFLogxK

U2 - 10.1109/ICWITE57052.2022.10176205

DO - 10.1109/ICWITE57052.2022.10176205

M3 - Conference contribution

AN - SCOPUS:85166466126

SN - 978-1-6654-6491-8

SP - 1

EP - 4

BT - 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE)

PB - Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship

Y2 - 1 December 2022 through 3 December 2022

ER -

Majumdar NK, Christa S, Manek AS, Sajeev S. Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm. In 2022 IEEE International Conference for Women in Innovation, Technology and Entrepreneurship (ICWITE) : Proceedings. United States: Institute of Electrical and Electronics Engineers. 2023. p. 1-4 doi: 10.1109/ICWITE57052.2022.10176205

Classification of Respiratory Diseases with Respiratory Sounds with Deep Learning Algorithm

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this