American Journal of Theoretical and Applied Statistics

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Early Diagnosis of Pneumonia from Chest X-Rays Using a Capsule Network Model: Enhancing Accuracy and Efficiency in Automated Image Classification

Received: 2 October 2023    Accepted: 20 October 2023    Published: 9 November 2023
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Abstract

Pneumonia is a significant public health concern worldwide, causing substantial morbidity and mortality. Early, accurate diagnosis is vital in ensuring timely treatment and improving patient outcomes. Chest X-ray analysis is the standard procedure used most frequently to diagnose pneumonia, but the accurate and timely interpretation of these images can be complex and time consuming. This research aimed to develop a capsule network (CapsNet) model for image classification, based on the Capsule network model introduced by Sabour and his colleagues in 2017 enabling automated chest X-ray analysis for early detection of pneumonia. Pneumonia impacts diverse populations, with vulnerable groups such as the elderly, young children and immunocompromised individuals at heightened risk. Delayed or missed diagnoses can lead to severe complications and increased healthcare costs. The reliance on human expertise for chest X-ray interpretation introduces the potential or errors, therefore there is a dire need to develop automated and precise diagnostic models and tools which are crucial for facilitating timely interventions. In this study secondary data obtained from Mendeley data was comprehensively pre-processed thoroughly by applying image resizing, standardization and normalization for consistent image quality, followed by a gaussian blur for noise reduction, and histogram equalization for contrast enhancement. The enhanced dataset enabled the main features of the pneumonia-infected images to be captured effectively during model training. The dataset was split into sets for training, testing and validation in an 80%, 10% and 10% ratio. The training set was used to train the CapsNet model which demonstrated a commendable performance with a 96% accuracy, a precision of 96.97% and a recall of 97.42%. The Capsule Network model shows a significant promise as a tool for improving the efficiency and accuracy of pneumonia diagnosis, thus befitting patients and healthcare providers.

DOI 10.11648/j.ajtas.20231206.12
Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6, November 2023)
Page(s) 161-173
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pneumonia, Capsnet, X-Ray, Occlusions, Topography, Deep Learning, Artificial Intelligence

References
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    Maureen, M. K., Mageto, T., Wanjoya, A. (2023). Early Diagnosis of Pneumonia from Chest X-Rays Using a Capsule Network Model: Enhancing Accuracy and Efficiency in Automated Image Classification. American Journal of Theoretical and Applied Statistics, 12(6), 161-173. https://doi.org/10.11648/j.ajtas.20231206.12

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    ACS Style

    Maureen, M. K.; Mageto, T.; Wanjoya, A. Early Diagnosis of Pneumonia from Chest X-Rays Using a Capsule Network Model: Enhancing Accuracy and Efficiency in Automated Image Classification. Am. J. Theor. Appl. Stat. 2023, 12(6), 161-173. doi: 10.11648/j.ajtas.20231206.12

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    AMA Style

    Maureen MK, Mageto T, Wanjoya A. Early Diagnosis of Pneumonia from Chest X-Rays Using a Capsule Network Model: Enhancing Accuracy and Efficiency in Automated Image Classification. Am J Theor Appl Stat. 2023;12(6):161-173. doi: 10.11648/j.ajtas.20231206.12

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  • @article{10.11648/j.ajtas.20231206.12,
      author = {Mbae Karwitha Maureen and Thomas Mageto and Anthony Wanjoya},
      title = {Early Diagnosis of Pneumonia from Chest X-Rays Using a Capsule Network Model: Enhancing Accuracy and Efficiency in Automated Image Classification},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {12},
      number = {6},
      pages = {161-173},
      doi = {10.11648/j.ajtas.20231206.12},
      url = {https://doi.org/10.11648/j.ajtas.20231206.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.12},
      abstract = {Pneumonia is a significant public health concern worldwide, causing substantial morbidity and mortality. Early, accurate diagnosis is vital in ensuring timely treatment and improving patient outcomes. Chest X-ray analysis is the standard procedure used most frequently to diagnose pneumonia, but the accurate and timely interpretation of these images can be complex and time consuming. This research aimed to develop a capsule network (CapsNet) model for image classification, based on the Capsule network model introduced by Sabour and his colleagues in 2017 enabling automated chest X-ray analysis for early detection of pneumonia. Pneumonia impacts diverse populations, with vulnerable groups such as the elderly, young children and immunocompromised individuals at heightened risk. Delayed or missed diagnoses can lead to severe complications and increased healthcare costs. The reliance on human expertise for chest X-ray interpretation introduces the potential or errors, therefore there is a dire need to develop automated and precise diagnostic models and tools which are crucial for facilitating timely interventions. In this study secondary data obtained from Mendeley data was comprehensively pre-processed thoroughly by applying image resizing, standardization and normalization for consistent image quality, followed by a gaussian blur for noise reduction, and histogram equalization for contrast enhancement. The enhanced dataset enabled the main features of the pneumonia-infected images to be captured effectively during model training. The dataset was split into sets for training, testing and validation in an 80%, 10% and 10% ratio. The training set was used to train the CapsNet model which demonstrated a commendable performance with a 96% accuracy, a precision of 96.97% and a recall of 97.42%. The Capsule Network model shows a significant promise as a tool for improving the efficiency and accuracy of pneumonia diagnosis, thus befitting patients and healthcare providers.
    },
     year = {2023}
    }
    

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    AB  - Pneumonia is a significant public health concern worldwide, causing substantial morbidity and mortality. Early, accurate diagnosis is vital in ensuring timely treatment and improving patient outcomes. Chest X-ray analysis is the standard procedure used most frequently to diagnose pneumonia, but the accurate and timely interpretation of these images can be complex and time consuming. This research aimed to develop a capsule network (CapsNet) model for image classification, based on the Capsule network model introduced by Sabour and his colleagues in 2017 enabling automated chest X-ray analysis for early detection of pneumonia. Pneumonia impacts diverse populations, with vulnerable groups such as the elderly, young children and immunocompromised individuals at heightened risk. Delayed or missed diagnoses can lead to severe complications and increased healthcare costs. The reliance on human expertise for chest X-ray interpretation introduces the potential or errors, therefore there is a dire need to develop automated and precise diagnostic models and tools which are crucial for facilitating timely interventions. In this study secondary data obtained from Mendeley data was comprehensively pre-processed thoroughly by applying image resizing, standardization and normalization for consistent image quality, followed by a gaussian blur for noise reduction, and histogram equalization for contrast enhancement. The enhanced dataset enabled the main features of the pneumonia-infected images to be captured effectively during model training. The dataset was split into sets for training, testing and validation in an 80%, 10% and 10% ratio. The training set was used to train the CapsNet model which demonstrated a commendable performance with a 96% accuracy, a precision of 96.97% and a recall of 97.42%. The Capsule Network model shows a significant promise as a tool for improving the efficiency and accuracy of pneumonia diagnosis, thus befitting patients and healthcare providers.
    
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Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

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