Prediction of Pulmonary Fibrosis Progression using CNN and Regression

J. Madhuri; Vikram Bhushan; Sai Kishore Hr; Kumar G. Sharan; J. Shreyas

doi:10.1109/ICAC3N53548.2021.9725730

Prediction of Pulmonary Fibrosis Progression using CNN and Regression

J. Madhuri, Vikram Bhushan, Sai Kishore Hr, Kumar G. Sharan, J. Shreyas

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

3 Citations (Scopus)

Abstract

The term idiopathic is used to refer to disease caused by unknown causes. Idiopathic pulmonary fibrosis is a chronic, progressive disease which affects the lungs and causes scar tissues to develop within them. This prevents the patient's lungs to transport oxygen into the bloodstream effectively. The daily routine of the patient is affected as the ease of breathing continues to decline. Early medical intervention and proper diagnosis can help keep the disease under control. The severity of the disease is measured used Forced Vital Capacity (FVC) values. In this paper we design an easy-to-use web application which collects the patient's CT scans, characteristic data and the initial FVC measurement. We predict the FVC values for up to a period of 2.5-3.0 years which effectively provides the Pulmonologist the rate of decline upon which suitable medications can be provided to stall the decline. We use Regression Techniques and CNN architectures to predict FVC values.

Original language	English
Title of host publication	Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021
Editors	Vishnu Sharma, Ritesh Srivastava, Manjeet Singh
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	944-950
Number of pages	7
ISBN (Electronic)	9781665438117
DOIs	https://doi.org/10.1109/ICAC3N53548.2021.9725730
Publication status	Published - 2021
Event	3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 - Greater Noida, India Duration: 17-12-2021 → 18-12-2021

Publication series

Name	Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

Conference

Conference	3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021
Country/Territory	India
City	Greater Noida
Period	17-12-21 → 18-12-21

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems and Management
Control and Optimization

Access to Document

10.1109/ICAC3N53548.2021.9725730

Cite this

Madhuri, J., Bhushan, V., Hr, S. K., Sharan, K. G., & Shreyas, J. (2021). Prediction of Pulmonary Fibrosis Progression using CNN and Regression. In V. Sharma, R. Srivastava, & M. Singh (Eds.), Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 (pp. 944-950). (Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC3N53548.2021.9725730

Madhuri, J. ; Bhushan, Vikram ; Hr, Sai Kishore et al. / Prediction of Pulmonary Fibrosis Progression using CNN and Regression. Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021. editor / Vishnu Sharma ; Ritesh Srivastava ; Manjeet Singh. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 944-950 (Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021).

@inproceedings{d3932deabdb24a8cab497d1cf016a26d,

title = "Prediction of Pulmonary Fibrosis Progression using CNN and Regression",

abstract = "The term idiopathic is used to refer to disease caused by unknown causes. Idiopathic pulmonary fibrosis is a chronic, progressive disease which affects the lungs and causes scar tissues to develop within them. This prevents the patient's lungs to transport oxygen into the bloodstream effectively. The daily routine of the patient is affected as the ease of breathing continues to decline. Early medical intervention and proper diagnosis can help keep the disease under control. The severity of the disease is measured used Forced Vital Capacity (FVC) values. In this paper we design an easy-to-use web application which collects the patient's CT scans, characteristic data and the initial FVC measurement. We predict the FVC values for up to a period of 2.5-3.0 years which effectively provides the Pulmonologist the rate of decline upon which suitable medications can be provided to stall the decline. We use Regression Techniques and CNN architectures to predict FVC values.",

author = "J. Madhuri and Vikram Bhushan and Hr, \{Sai Kishore\} and Sharan, \{Kumar G.\} and J. Shreyas",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021 ; Conference date: 17-12-2021 Through 18-12-2021",

year = "2021",

doi = "10.1109/ICAC3N53548.2021.9725730",

language = "English",

series = "Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "944--950",

editor = "Vishnu Sharma and Ritesh Srivastava and Manjeet Singh",

booktitle = "Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021",

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}

Madhuri, J, Bhushan, V, Hr, SK, Sharan, KG & Shreyas, J 2021, Prediction of Pulmonary Fibrosis Progression using CNN and Regression. in V Sharma, R Srivastava & M Singh (eds), Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021. Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, Institute of Electrical and Electronics Engineers Inc., pp. 944-950, 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021, Greater Noida, India, 17-12-21. https://doi.org/10.1109/ICAC3N53548.2021.9725730

Prediction of Pulmonary Fibrosis Progression using CNN and Regression. / Madhuri, J.; Bhushan, Vikram; Hr, Sai Kishore et al.
Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021. ed. / Vishnu Sharma; Ritesh Srivastava; Manjeet Singh. Institute of Electrical and Electronics Engineers Inc., 2021. p. 944-950 (Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021).

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

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AU - Madhuri, J.

AU - Bhushan, Vikram

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AU - Sharan, Kumar G.

AU - Shreyas, J.

PY - 2021

Y1 - 2021

N2 - The term idiopathic is used to refer to disease caused by unknown causes. Idiopathic pulmonary fibrosis is a chronic, progressive disease which affects the lungs and causes scar tissues to develop within them. This prevents the patient's lungs to transport oxygen into the bloodstream effectively. The daily routine of the patient is affected as the ease of breathing continues to decline. Early medical intervention and proper diagnosis can help keep the disease under control. The severity of the disease is measured used Forced Vital Capacity (FVC) values. In this paper we design an easy-to-use web application which collects the patient's CT scans, characteristic data and the initial FVC measurement. We predict the FVC values for up to a period of 2.5-3.0 years which effectively provides the Pulmonologist the rate of decline upon which suitable medications can be provided to stall the decline. We use Regression Techniques and CNN architectures to predict FVC values.

AB - The term idiopathic is used to refer to disease caused by unknown causes. Idiopathic pulmonary fibrosis is a chronic, progressive disease which affects the lungs and causes scar tissues to develop within them. This prevents the patient's lungs to transport oxygen into the bloodstream effectively. The daily routine of the patient is affected as the ease of breathing continues to decline. Early medical intervention and proper diagnosis can help keep the disease under control. The severity of the disease is measured used Forced Vital Capacity (FVC) values. In this paper we design an easy-to-use web application which collects the patient's CT scans, characteristic data and the initial FVC measurement. We predict the FVC values for up to a period of 2.5-3.0 years which effectively provides the Pulmonologist the rate of decline upon which suitable medications can be provided to stall the decline. We use Regression Techniques and CNN architectures to predict FVC values.

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M3 - Conference contribution

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BT - Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

Y2 - 17 December 2021 through 18 December 2021

ER -

Madhuri J, Bhushan V, Hr SK, Sharan KG, Shreyas J. Prediction of Pulmonary Fibrosis Progression using CNN and Regression. In Sharma V, Srivastava R, Singh M, editors, Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 944-950. (Proceedings - 2021 3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021). doi: 10.1109/ICAC3N53548.2021.9725730

Prediction of Pulmonary Fibrosis Progression using CNN and Regression

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