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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM47868

Pneumocare : From Care to Cure

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM47868

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM47868

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AKJ

Abhay Krishna Joshi1

KPM

Kannika P Menasinkai2

KG

Karthik Gubber3

AK

Anjali Kabadi4

APD

Anjali Parashuram Dodmani5

DJP

Dr. Jagadeesh Pujari6

PVJ

Prof Varsha Jadhav7

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Abstract

PneumoCare is an advanced AI-driven system designed to detect and diagnose respiratory conditions, including normal, pneumonia and covid pneumonia , from chest X-ray images using deep learning techniques. The system utilizes convolutional neural networks(CNN) to automatically identify and classify regions affected by these conditions, offering valuable assistance to healthcare professionals in providing faster and more accurate diagnoses. This project presents a cost-effective and scalable solution ,particularly for areas with limited access to medical resources ,ensuring timely and precise detection of respiratory conditions in clinical settings .with its integration into medical imaging technologies, PneumoCare aims to enhance diagnostic capabilities, ultimately improving patient outcomes and reducing the global burden of respiratory diseases.

KEYWORDS

PneumoCare respiratory healthcare CNN AI diagnosis chest X-rays pneumonia detection covid pneumonia.

Cite This Article

Abhay Krishna Joshi1, Kannika P Menasinkai2, Karthik Gubber3, Anjali Kabadi4, Anjali Parashuram Dodmani5, Dr. Jagadeesh Pujari6, Prof Varsha Jadhav7, "Pneumocare : From Care to Cure," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM47868

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Publication Date

5/15/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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1

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2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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