Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images

• Author(s): Mamta Kumari; Aman; Manya Juneja; Aman Atri
• Paper ID: 1719792
• Page: 2676-2682
• Published Date: 31-05-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 11 May-2025
• DOI: https://doi.org/10.64388/IREV8I11-1719792

Abstract

Pneumonia still constitutes one of the major causes of mortality across the globe, specifically targeting children, elderly people, and immunocompromised patients. Diagnosis and timely intervention can help reduce disease burden and patient morbidity. Traditional methods of diagnosing pneumonia using chest X-ray images depend largely on the involvement of radiologists, making the entire process slow and prone to observer variability, especially in limited health care facilities. This paper introduces an automated pneumonia diagnosis system based on deep learning using ResNet34 architecture. The model uses image pre-processing techniques, transfer learning, and data augmentation to boost feature extraction capabilities while reducing overfitting. The trained system classifies chest X-ray images into pneumonia and non-pneumonia classes using a user-friendly web application built on the Streamlit platform. The designed system provides a good computer-aided diagnosis system which may help medical professionals conduct initial screening for pneumonia, reduce diagnostic effort, and increase the accessibility of health care facilities in rural areas.

Keywords

Pneumonia Detection, Deep Learning, Convolutional Neural Network, ResNet34, Transfer Learning, Chest X-ray, Medical Image Analysis, Artificial Intelligence, Computer-Aided Diagnosis.

Citations

IRE Journals:
Mamta Kumari, Aman, Manya Juneja, Aman Atri "Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 2676-2682 https://doi.org/10.64388/IREV8I11-1719792

IEEE:
Mamta Kumari, Aman, Manya Juneja, Aman Atri "Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images" Iconic Research And Engineering Journals, vol. 8, no. 11, May. 2025, doi: https://doi.org/10.64388/IREV8I11-1719792

APA:
Mamta Kumari, Aman, Manya Juneja, Aman Atri (2025). Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images. Iconic Research And Engineering Journals, 8(11). doi: https://doi.org/10.64388/IREV8I11-1719792

MLA:
Mamta Kumari, Aman, Manya Juneja, Aman Atri "Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images" Iconic Research And Engineering Journals, vol. 8, no. 11, May. 2025. Crossref, https://doi.org/10.64388/IREV8I11-1719792

BibTeX

@article{1719792,
author = {Mamta Kumari, Aman, Manya Juneja, Aman Atri},
title = {Healthcare Diagnosis System Using Deep Learning-Based Image Analysis for Automated Pneumonia Detection from Chest X-Ray Images},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {2676-2682},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719792.pdf},
abstract = {Pneumonia still constitutes one of the major causes of mortality across the globe, specifically targeting children, elderly people, and immunocompromised patients. Diagnosis and timely intervention can help reduce disease burden and patient morbidity. Traditional methods of diagnosing pneumonia using chest X-ray images depend largely on the involvement of radiologists, making the entire process slow and prone to observer variability, especially in limited health care facilities. This paper introduces an automated pneumonia diagnosis system based on deep learning using ResNet34 architecture. The model uses image pre-processing techniques, transfer learning, and data augmentation to boost feature extraction capabilities while reducing overfitting. The trained system classifies chest X-ray images into pneumonia and non-pneumonia classes using a user-friendly web application built on the Streamlit platform. The designed system provides a good computer-aided diagnosis system which may help medical professionals conduct initial screening for pneumonia, reduce diagnostic effort, and increase the accessibility of health care facilities in rural areas.},
keywords = {Pneumonia Detection, Deep Learning, Convolutional Neural Network, ResNet34, Transfer Learning, Chest X-ray, Medical Image Analysis, Artificial Intelligence, Computer-Aided Diagnosis.},
month = {May}
}