| Peer-Reviewed

Classification of Pneumonia Using Deep Convolutional Neural Network

Received: 13 March 2022     Accepted: 6 April 2022     Published: 14 April 2022
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Abstract

Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.

Published in American Journal of Computer Science and Technology (Volume 5, Issue 2)
DOI 10.11648/j.ajcst.20220502.11
Page(s) 26-33
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), 2022. Published by Science Publishing Group

Keywords

Deep Learning, Convolutional Neural Network, Transfer Learning, Support Vector Machine, Chest X-ray, Pneumonia

References
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Cite This Article
  • APA Style

    Alhussein Mohammed Ahmed, Gais Alhadi Babikir, Salma Mohammed Osman. (2022). Classification of Pneumonia Using Deep Convolutional Neural Network. American Journal of Computer Science and Technology, 5(2), 26-33. https://doi.org/10.11648/j.ajcst.20220502.11

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    ACS Style

    Alhussein Mohammed Ahmed; Gais Alhadi Babikir; Salma Mohammed Osman. Classification of Pneumonia Using Deep Convolutional Neural Network. Am. J. Comput. Sci. Technol. 2022, 5(2), 26-33. doi: 10.11648/j.ajcst.20220502.11

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    AMA Style

    Alhussein Mohammed Ahmed, Gais Alhadi Babikir, Salma Mohammed Osman. Classification of Pneumonia Using Deep Convolutional Neural Network. Am J Comput Sci Technol. 2022;5(2):26-33. doi: 10.11648/j.ajcst.20220502.11

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  • @article{10.11648/j.ajcst.20220502.11,
      author = {Alhussein Mohammed Ahmed and Gais Alhadi Babikir and Salma Mohammed Osman},
      title = {Classification of Pneumonia Using Deep Convolutional Neural Network},
      journal = {American Journal of Computer Science and Technology},
      volume = {5},
      number = {2},
      pages = {26-33},
      doi = {10.11648/j.ajcst.20220502.11},
      url = {https://doi.org/10.11648/j.ajcst.20220502.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.11},
      abstract = {Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Classification of Pneumonia Using Deep Convolutional Neural Network
    AU  - Alhussein Mohammed Ahmed
    AU  - Gais Alhadi Babikir
    AU  - Salma Mohammed Osman
    Y1  - 2022/04/14
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ajcst.20220502.11
    DO  - 10.11648/j.ajcst.20220502.11
    T2  - American Journal of Computer Science and Technology
    JF  - American Journal of Computer Science and Technology
    JO  - American Journal of Computer Science and Technology
    SP  - 26
    EP  - 33
    PB  - Science Publishing Group
    SN  - 2640-012X
    UR  - https://doi.org/10.11648/j.ajcst.20220502.11
    AB  - Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.
    VL  - 5
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Science, Faculty of Mathematics and Computer Sciences, University of Gezira, Wad Madani, Sudan

  • Department of Computer Science, Faculty of Mathematics and Computer Sciences, University of Gezira, Wad Madani, Sudan

  • Department of Computer Science, Faculty of Mathematics and Computer Sciences, University of Gezira, Wad Madani, Sudan

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