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

Author

Listed:
  • Sravani Nalluri

    (VIT University School of Computer Science and Engineering)

  • R. Sasikala

    (VIT University School of Computer Science and Engineering)

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.

Suggested Citation

  • Sravani Nalluri & R. Sasikala, 2024. "A deep neural architecture for SOTA pneumonia detection from chest X-rays," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 489-502, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01788-x
    DOI: 10.1007/s13198-022-01788-x
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