International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Pneumonia Detection from Chest X-ray Images Based on Sequential Model

  • Alshehri, Asma (Department of Computer Science, Umm Al-Qura University) ;
  • Alharbi, Bayan (Department of Computer Science, Umm Al-Qura University) ;
  • Alharbi, Amirah (Department of Computer Science, Umm Al-Qura University)
  • Received : 2022.04.05
  • Published : 2022.04.30
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Abstract

Pneumonia is a form of acute respiratory infection that affects the lungs. According to the World Health Organization, pneumonia is the leading cause of death for children worldwide. As a result, pneumonia was the top killer of children under the age of five years old in 2015, which is 15% of all deaths worldwide. In this paper, we used CNN model architectures to compare between the result of proposed a CNN method with VGG based model architecture. The model's performance in detecting pneumonia shows that the proposed model based on VGG can classify normal and abnormal X-rays effectively and more accurately than the proposed model used in this paper.

Keywords

Acknowledgement

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4310140DSR01).

References

  1. Zhang, D.; Ren, F.; Li, Y.; Na, L.; Ma, Y. Pneumonia Detection from Chest X-ray Images Based on Convolutional Neural Network. Electronics 2021, 10, 1512. https://doi.org/10.3390/electronics10131512
  2. UNICEF DATA Percentage of deaths caused by pneumonia in children under 5 years of age (. https://data.unicef.org/topic/child-health/pneumonia/. 2021-7-20.
  3. Wang, X.; Peng, Y.; Lu, L.; Lu, Z.; Bagheri, M.; Summers, R.M. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097-2106.
  4. Gu, Y.; Lu, X.; Yang, L.; Zhang, B.; Yu, D.; Zhao, Y.; Gao, L.; Wu, L.; Zhou, T. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Computers in biology and medicine 2018, 103, 220-231. https://doi.org/10.1016/j.compbiomed.2018.10.011
  5. Setio, A.A.A.; Traverso, A.; De Bel, T.; Berens, M.S.; Van Den Bogaard, C.; Cerello, P.; Chen, H.; Dou, Q.; Fantacci, M.E.; Geurts, B.; others. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical image analysis 2017, 42, 1-13. https://doi.org/10.1016/j.media.2017.06.015
  6. Zhu, W.; Liu, C.; Fan, W.; Xie, X. Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018, pp. 673-681.
  7. Kong, W.; Hong, J.; Jia, M.; Yao, J.; Cong, W.; Hu, H.; Zhang, H. YOLOv3-DPFIN: a dual-path feature fusion neural network for robust real-time sonar target detection. IEEE Sensors Journal 2019, 20, 3745-3756. https://doi.org/10.1109/jsen.2019.2960796
  8. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computerassisted intervention. Springer, 2015, pp. 234-241.
  9. Kallianos, K.; Mongan, J.; Antani, S.; Henry, T.; Taylor, A.; Abuya, J.; Kohli, M. How far have we come? Artificial intelligence for chest radiograph interpretation. Clinical radiology 2019, 74, 338-345. https://doi.org/10.1016/j.crad.2018.12.015
  10. LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation applied to handwritten zip code recognition. Neural computation 1989, 1, 541-551. https://doi.org/10.1162/neco.1989.1.4.541