Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification

Hannah Kariuki; Samuel Mwalili; Anthony Waititu

doi:doi:10.11648/j.ijdsa.20241001.12

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Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification

Hannah Kariuki^*, Samuel Mwalili, Anthony Waititu

Published in International Journal of Data Science and Analysis (Volume 10, Issue 1)

Received: 23 October 2023 Accepted: 6 March 2024 Published: 19 March 2024

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Abstract

The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.

Published in	International Journal of Data Science and Analysis (Volume 10, Issue 1)
DOI	10.11648/j.ijdsa.20241001.12
Page(s)	11-19
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

Deep Convolution Neural Networks, Data Imbalance, Data Augmentation, Chest X-Ray Imaging

References

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APA Style

Kariuki, H., Mwalili, S., Waititu, A. (2024). Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. International Journal of Data Science and Analysis, 10(1), 11-19. https://doi.org/10.11648/j.ijdsa.20241001.12

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ACS Style

Kariuki, H.; Mwalili, S.; Waititu, A. Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. Int. J. Data Sci. Anal. 2024, 10(1), 11-19. doi: 10.11648/j.ijdsa.20241001.12

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AMA Style

Kariuki H, Mwalili S, Waititu A. Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. Int J Data Sci Anal. 2024;10(1):11-19. doi: 10.11648/j.ijdsa.20241001.12

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@article{10.11648/j.ijdsa.20241001.12,
  author = {Hannah Kariuki and Samuel Mwalili and Anthony Waititu},
  title = {Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {1},
  pages = {11-19},
  doi = {10.11648/j.ijdsa.20241001.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241001.12},
  abstract = {The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.
},
 year = {2024}
}

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TY  - JOUR
T1  - Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification
AU  - Hannah Kariuki
AU  - Samuel Mwalili
AU  - Anthony Waititu
Y1  - 2024/03/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241001.12
DO  - 10.11648/j.ijdsa.20241001.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 11
EP  - 19
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241001.12
AB  - The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.

VL  - 10
IS  - 1
ER  -

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Author Information

Hannah Kariuki

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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Samuel Mwalili

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Anthony Waititu

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email

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Plain Text BibTeX RIS

APA Style

Kariuki, H., Mwalili, S., Waititu, A. (2024). Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. International Journal of Data Science and Analysis, 10(1), 11-19. https://doi.org/10.11648/j.ijdsa.20241001.12

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ACS Style

Kariuki, H.; Mwalili, S.; Waititu, A. Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. Int. J. Data Sci. Anal. 2024, 10(1), 11-19. doi: 10.11648/j.ijdsa.20241001.12

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AMA Style

Kariuki H, Mwalili S, Waititu A. Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification. Int J Data Sci Anal. 2024;10(1):11-19. doi: 10.11648/j.ijdsa.20241001.12

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@article{10.11648/j.ijdsa.20241001.12,
  author = {Hannah Kariuki and Samuel Mwalili and Anthony Waititu},
  title = {Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {1},
  pages = {11-19},
  doi = {10.11648/j.ijdsa.20241001.12},
  url = {https://doi.org/10.11648/j.ijdsa.20241001.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241001.12},
  abstract = {The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Deep Convolutional Neural Networks with Augmentation for Chest X-Ray Classification
AU  - Hannah Kariuki
AU  - Samuel Mwalili
AU  - Anthony Waititu
Y1  - 2024/03/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241001.12
DO  - 10.11648/j.ijdsa.20241001.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 11
EP  - 19
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241001.12
AB  - The recent release of large amounts of Chest radiographs (CXR) has prompted the research of automated analysis of Chest X-rays to improve health care services. DCNNs are well suited for image classification because they can learn to extract features from images that are relevant to the task at hand. However, class imbalance is a common problem in chest X-ray imaging, where the number of samples for some disease category is much lower than the number of samples in other categories. This can occur as a result of rarity of some diseases being studied or the fact that only a subset of patients with a particular disease may undergo imaging. Class imbalance can make it difficult for Deep Convolutional Neural networks (DCNNs) to learn and make accurate predictions on the minority classes. Obtaining more data for minority groups is not feasible in medical research. Therefore, there is a need for a suitable method that can address class imbalance. To address class imbalance in DCNNs, this study proposes, Deep Convolutional Neural Networks with Augmentation. The results show that data augmentation can be applied to imbalanced dataset to increase the representation of the minority class by generating new images that are a slight variation of the original CXR images. This study further evaluates identifiability and consistency of the proposed model.

VL  - 10
IS  - 1
ER  -

Copy | Download