Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images

Hannah Kim; Gina Kim

doi:doi:10.11648/j.ijmi.20210901.18

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Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images

Hannah Kim, Gina Kim

Published in International Journal of Medical Imaging (Volume 9, Issue 1)

Received: 25 January 2021 Accepted: 2 February 2021 Published: 4 March 2021

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Abstract

Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.

Published in	International Journal of Medical Imaging (Volume 9, Issue 1)
DOI	10.11648/j.ijmi.20210901.18
Page(s)	79-86
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

COVID-19, Chest X-ray, Hybrid Model, CNN, Bi-LSTM, Optimizers

References

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APA Style

Hannah Kim, Gina Kim. (2021). Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. International Journal of Medical Imaging, 9(1), 79-86. https://doi.org/10.11648/j.ijmi.20210901.18

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ACS Style

Hannah Kim; Gina Kim. Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. Int. J. Med. Imaging 2021, 9(1), 79-86. doi: 10.11648/j.ijmi.20210901.18

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AMA Style

Hannah Kim, Gina Kim. Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. Int J Med Imaging. 2021;9(1):79-86. doi: 10.11648/j.ijmi.20210901.18

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@article{10.11648/j.ijmi.20210901.18,
  author = {Hannah Kim and Gina Kim},
  title = {Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images},
  journal = {International Journal of Medical Imaging},
  volume = {9},
  number = {1},
  pages = {79-86},
  doi = {10.11648/j.ijmi.20210901.18},
  url = {https://doi.org/10.11648/j.ijmi.20210901.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20210901.18},
  abstract = {Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.},
 year = {2021}
}

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TY  - JOUR
T1  - Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images
AU  - Hannah Kim
AU  - Gina Kim
Y1  - 2021/03/04
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmi.20210901.18
DO  - 10.11648/j.ijmi.20210901.18
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 79
EP  - 86
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20210901.18
AB  - Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.
VL  - 9
IS  - 1
ER  -

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Author Information

Hannah Kim

Yongsan International School of Seoul, Seoul, Republic of Korea
Gina Kim

Yongsan International School of Seoul, Seoul, Republic of Korea

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Plain Text BibTeX RIS

APA Style

Hannah Kim, Gina Kim. (2021). Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. International Journal of Medical Imaging, 9(1), 79-86. https://doi.org/10.11648/j.ijmi.20210901.18

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ACS Style

Hannah Kim; Gina Kim. Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. Int. J. Med. Imaging 2021, 9(1), 79-86. doi: 10.11648/j.ijmi.20210901.18

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AMA Style

Hannah Kim, Gina Kim. Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images. Int J Med Imaging. 2021;9(1):79-86. doi: 10.11648/j.ijmi.20210901.18

Copy | Download

@article{10.11648/j.ijmi.20210901.18,
  author = {Hannah Kim and Gina Kim},
  title = {Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images},
  journal = {International Journal of Medical Imaging},
  volume = {9},
  number = {1},
  pages = {79-86},
  doi = {10.11648/j.ijmi.20210901.18},
  url = {https://doi.org/10.11648/j.ijmi.20210901.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20210901.18},
  abstract = {Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.},
 year = {2021}
}

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TY  - JOUR
T1  - Novel Method to Diagnose COVID-19: HNN of CNN and Bi-LSTM Using X-ray Images
AU  - Hannah Kim
AU  - Gina Kim
Y1  - 2021/03/04
PY  - 2021
N1  - https://doi.org/10.11648/j.ijmi.20210901.18
DO  - 10.11648/j.ijmi.20210901.18
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 79
EP  - 86
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20210901.18
AB  - Coronavirus Disease (COVID-19) has caused a global pandemic and many of COVID-19’s key symptoms are related to the respiratory tract. In fact, the most relevant features correlated to the diagnosis of COVID-19 were found to be breathing problems and dry cough as determined by experimental results, produced when such a dataset was run through the random forest model with feature importance function. Therefore, using chest x-ray images labeled as COVID-19 and normal from kaggle, we developed a novel hybrid deep learning model incorporating CNN (convolutional neural network) and Bi-LSTM (bidirectional long short term memory) to detect symptoms of COVID-19. Our goal was to develop a model with the highest accuracy. As a total number of datasets were not enough to train the model, we augmented the input dataset through the “ImagedataGenerator” function from the Keras. Also, this proposed model ensures high accuracy as experimental results reported its average accuracy, which was tested with various optimizers (Adam, Nadam, Rmsprop, SGD), to be 98.13%. The new model showed the highest average accuracy compared to any other preexisting models (VGG-16, Resnet50, Resent50_v2, Mobilenet, Mobilenet_v2, Xception) also tested during this research. This model could potentially be used as an alternative process to diagnose COVID-19, especially with the number of global cases increasing, along with the need for efficient, quicker testing methods.
VL  - 9
IS  - 1
ER  -

Copy | Download