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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM53838

Pneumonia Detection Using Web Mern Stack

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM53838

Volume:

9 |

11 |

Nov 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

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

KK

KISHORE K#1

PKA

PRADEEP KUMAR A#2

LKG

LOGESH KUMAR G#3

XMA

XAVIER MARY A#4

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Abstract

The Automated Pneumonia Prediction System is an intelligent, efficient, and user-friendly platform designed to streamline and automate the diagnosis process for clinicians in healthcare institutions. It replaces traditional manual interpretation of chest X-rays with an advanced AI-driven workflow, enabling rapid and accurate pneumonia detection. Clinicians can securely upload patient images and details, receive real-time diagnostic predictions, and review results through an interactive dashboard. Integrated modules for data validation, privacy, and monitoring ensure compliance and security at every step. The system supports seamless integration with electronic medical records (EMR), facilitating automated feedback, continuous model updates, and transparent clinical decision support. Notifications and reporting tools keep healthcare providers informed about new cases and prediction outcomes, enhancing workflow efficiency, diagnostic reliability, and patient care.

KEYWORDS

Pneumonia Detection Clinical Automation AI Diagnosis System Chest X-ray Analysis Physician Portal Healthcare Workflow Deep Learning Paperless Diagnosis Medical Data Integration Real- Time Prediction EMR Interoperability.

Cite This Article

KISHORE K#1, PRADEEP KUMAR A#2, LOGESH KUMAR G#3, XAVIER MARY A#4, "Pneumonia Detection Using Web Mern Stack," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 11, Nov 2025.

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Publication Date

12/10/2025

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Double-Blind Peer Review

Review Rounds: 2

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

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