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A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images

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Abstract

Pneumonia is a disease caused by bacteria, viruses, and fungi that settle in the alveolar sacs of the lungs and can lead to serious health complications in humans. Early detection of pneumonia is necessary for early treatment to manage and cure the disease. Recently, machine learning-based pneumonia detection methods have focused on pneumonia in adults. Machine learning relies on manual feature engineering, whereas deep learning can automatically detect and extract features from data. This study proposes a deep learning feature extraction-based hybrid approach that combines deep learning and machine learning to detect pediatric pneumonia, which is difficult to standardize. The proposed hybrid approach enhances the accuracy of detecting pediatric pneumonia and simplifies the approach by eliminating the requirement for advanced feature extraction. The experiments indicate that the hybrid approach using a Medium Neural Network based on AlexNet feature extraction achieved a 97.9% accuracy rate and 98.0% sensitivity rate. The results show that the proposed approach achieved higher accuracy rates than state-of-the-art approaches.

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Funding

The authors have declared that no funds, grants, or other support has been received during the preparation of this manuscript.

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Authors and Affiliations

  1. Osmaniye Korkut Ata University Electrical and Electronics Engineering Department, Osmaniye, Turkey

    Ufuk Bal

  2. Manisa Celal Bayar University Pediatrics Department, Manisa, Turkey

    Alkan Bal & Nida Gürbüz

  3. Vocational School of Technical Sciences, Electronics Technology, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Özge Taylan Moral

  4. Department of Radiology, Manisa Celal Bayar University, Manisa, Turkey

    Fatih Düzgün

Authors
  1. Ufuk Bal
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  2. Alkan Bal
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  3. Özge Taylan Moral
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  4. Fatih Düzgün
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  5. Nida Gürbüz
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Contributions

All authors contributed to the study conception and design.

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Correspondence to Ufuk Bal.

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The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Manisa Celal Bayar University Clinical Research Ethics Committee approved this study on September 21, 2020.

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Informed consent was obtained from all individual participants included in the study.

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The authors affirm that human research participants provided informed consent for publication of the images in Figs. 1, 2, and 3.

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Bal, U., Bal, A., Moral, Ö.T. et al. A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images. Phys Eng Sci Med 47, 109–117 (2024). https://doi.org/10.1007/s13246-023-01347-z

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  • DOI: https://doi.org/10.1007/s13246-023-01347-z

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