To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.
| Published in | Advances in Bioscience and Bioengineering (Volume 9, Issue 2) |
| DOI | 10.11648/j.abb.20210902.13 |
| Page(s) | 32-38 |
| 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 |
COVID-19, Chest X-ray, Xception, CNN, Bi-LSTM, CT-scan
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APA Style
Eric Dawson Oh, Jeong Gyoun Song, Yeon-Joon Jordan Kim, Wonse Kim. (2021). Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Advances in Bioscience and Bioengineering, 9(2), 32-38. https://doi.org/10.11648/j.abb.20210902.13
ACS Style
Eric Dawson Oh; Jeong Gyoun Song; Yeon-Joon Jordan Kim; Wonse Kim. Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Adv. BioSci. Bioeng. 2021, 9(2), 32-38. doi: 10.11648/j.abb.20210902.13
AMA Style
Eric Dawson Oh, Jeong Gyoun Song, Yeon-Joon Jordan Kim, Wonse Kim. Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM. Adv BioSci Bioeng. 2021;9(2):32-38. doi: 10.11648/j.abb.20210902.13
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Download@article{10.11648/j.abb.20210902.13,
author = {Eric Dawson Oh and Jeong Gyoun Song and Yeon-Joon Jordan Kim and Wonse Kim},
title = {Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM},
journal = {Advances in Bioscience and Bioengineering},
volume = {9},
number = {2},
pages = {32-38},
doi = {10.11648/j.abb.20210902.13},
url = {https://doi.org/10.11648/j.abb.20210902.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.abb.20210902.13},
abstract = {To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand.},
year = {2021}
}
TY - JOUR T1 - Diagnosing COVID-19 with Xception + Bi-LSTM: Detecting Anomalies with Grad-CAM AU - Eric Dawson Oh AU - Jeong Gyoun Song AU - Yeon-Joon Jordan Kim AU - Wonse Kim Y1 - 2021/06/03 PY - 2021 N1 - https://doi.org/10.11648/j.abb.20210902.13 DO - 10.11648/j.abb.20210902.13 T2 - Advances in Bioscience and Bioengineering JF - Advances in Bioscience and Bioengineering JO - Advances in Bioscience and Bioengineering SP - 32 EP - 38 PB - Science Publishing Group SN - 2330-4162 UR - https://doi.org/10.11648/j.abb.20210902.13 AB - To successfully contain the spread of COVID-19, the importance of swift and accurate testing is unparalleled. Currently, PCR tests are most commonly utilized to detect COVID-19, yet these tests typically consume 24 hours—not a short period of time. Hence, new deep learning algorithms have been under development to accurately and quickly detect COVID-19. With this aim, we have proposed a deep learning model to determine the presence of COVID-19 using X-ray images by combining Xception with Bi-LSTM. Altering the output from the Xception network into a three-dimensional shape rendered the ensuing Bi-LSTM network compatible. Consequently, the novel model yielded a high accuracy rate of 98.5%, one greater than the accuracy rates of VGG16, Densenet, Mobilenet, Mobilenet_v2, Resnet50, and DNN models. Moreover, with the creation of a heatmap, by using a Class Activation Map, our model could specifically locate the anomaly. However, our model could not yield high accuracy when we applied it to the lung ct scan dataset. Even though training and validation accuracy kept rising, the test accuracy was far lower than them. Furthermore, with limitations including a small sample size, inflated accuracy rates for binary classification, and incompatibility with CT images, follow-up research will need to ensue to perfect the model at hand. VL - 9 IS - 2 ER -
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