A deep neural architecture for SOTA pneumonia detection from chest X-rays

Sravani Nalluri and R. Sasikala ()
Additional contact information
Sravani Nalluri: VIT University School of Computer Science and Engineering
R. Sasikala: VIT University School of Computer Science and Engineering

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 42, 489-502

Abstract: Abstract Pneumonia among children is a leading cause of death in India, and it gains a lot of researchers' attention to develop early detection tools. Due to a lack of the number of radiologists, especially in rural India, the traditional method of diagnosing pneumonia does not address the real-time issues related to early stages. This paper presents a deep learning model, NASNet (Neural Architecture Search Network), pre-trained on ImageNet to predict pneumonia very early stage through chest x-rays of patients. With 2.6 million trainable parameters, the proposed model can run even on a mobile phone with good precision, recall, and an F1 score to detect pneumonia. This approach thus proves to be significantly better than the current state-of-the-art models. It can also help trained radiologists to get a second opinion/ validation of pneumonia diagnosis.

Keywords: Deep learning; Pneumonia; Chest X-Ray; NASNet (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-022-01788-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01788-x

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-022-01788-x

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().