(2) Suteera Vonganansup (Department of Computer Science Faculty of Business Administration and Information Technology Rajamangala University of Technology Tawan-Ok, Thailand)
(3) * Prinda Labcharoenwongs (Department of Computer Science Faculty of Business Administration and Information Technology Rajamangala University of Technology Tawan-Ok, Thailand)
*corresponding author
AbstractChest radiography (CXR) image is usually required for lung severity assessment. However, chest X-rays in COVID-19 interpretation is required expert radiologists’ knowledge. This study aims to improve the COVID-19 X-ray image classification using feature selection technique by the regression mutual information deep convolution neuron networks (RMI Deep-CNNs). The dataset consists of 219 COVID-19, 500 viral pneumonias, and 500 normal chest X-ray images. CXR images were comprehensively pre-trained using DCNNs to extract the very large image features, then, the feature selection could reduce the complexity of a model and reduce the model overfitting. Therefore, the critical features were selected using regression mutual information followed by the fully connected with softmax layer for classification. For the classification of two alternative systems, these networks were compared (ResNet152V2 and InceptionV3). The classification performance for both schemes were 92.21%, 100%, 90% and 91.39%, 100%, 82.50%, respectively. In addition, RMI Deep-CNNs not only improve the accuracy but also reduce trainable features by over 80%. This approach tends to significantly improve the computation time and model accuracy for COVID‐19 classification.
KeywordsCOVID-19; Medical imaging; Deep neural networks; Regression mutual information; Feature selection
|
DOIhttps://doi.org/10.26555/ijain.v8i2.809 |
Article metricsAbstract views : 976 | PDF views : 194 |
Cite |
Full TextDownload |
References
[1] W. Wang et al., “Detection of SARS-CoV-2 in Different Types of Clinical Specimens,” JAMA, vol. 323, no. 18, pp. 1843–1844, Mar. 2020, doi: 10.1001/jama.2020.3786.
[2] T. Yang, Y.-C. Wang, C.-F. Shen, and C.-M. Cheng, “Point of Care RNA-Based Diagnostic Device for COVID-19,” Diagnostics, vol. 10, no. 3, pp. 1–3, Mar. 2020, doi: 10.3390/diagnostics10030165.
[3] D. Wang et al., “Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China,” JAMA, vol. 323, no. 11, pp. 1061–1069, Mar. 2020, doi: 10.1001/jama.2020.1585.
[4] N. Chen et al., “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study,” Lancet, vol. 395, no. 10223, pp. 507–513, Feb. 2020, doi: 10.1016/S0140-6736(20)30211-7.
[5] Q. Li et al., “Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia,” N. Engl. J. Med., vol. 382, no. 13, pp. 1199–1207, 2020. Available at: Google Scholar
[6] C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet, vol. 395, no. 10223, pp. 497–506, Feb. 2020, doi: 10.1016/S0140-6736(20)30183-5.
[7] V. Chouhan et al., “A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images,” Appl. Sci., vol. 10, no. 2, pp. 1–17, Jan. 2020, doi: 10.3390/app10020559.
[8] Z. Wu, C. Shen, and A. van den Hengel, “Wider or Deeper: Revisiting the ResNet Model for Visual Recognition,” Pattern Recognit., vol. 90, pp. 119–133, Jun. 2019, doi: 10.1016/j.patcog.2019.01.006.
[9] J. M. Ahn, S. Kim, K.-S. Ahn, S.-H. Cho, K. B. Lee, and U. S. Kim, “A deep learning model for the detection of both advanced and early glaucoma using fundus photography,” PLoS One, vol. 13, no. 11, p. e0207982, Nov. 2018, doi: 10.1371/journal.pone.0207982.
[10] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, Dec. 2015, doi: 10.1007/s11263-015-0816-y.
[11] X. Gu, L. Pan, H. Liang, and R. Yang, “Classification of Bacterial and Viral Childhood Pneumonia Using Deep Learning in Chest Radiography,” in Proceedings of the 3rd International Conference on Multimedia and Image Processing - ICMIP 2018, 2018, pp. 88–93, doi: 10.1145/3195588.3195597.
[12] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Sci. Rep., vol. 10, no. 1, pp. 1–12, Dec. 2020, doi: 10.1038/s41598-020-76550-z.
[13] A. S. Joaquin, “Using deep learning to detect pneumonia caused by ncov-19 from x-ray images,” Towards data science. 2020. Available at: Google Scholar
[14] R. Shwartz-Ziv and N. Tishby, “Opening the black box of deep neural networks via information,” Arxiv preprint. pp. 1–19, 2017. Available at: Google Scholar
[15] A. M. Saxe et al., “On the information bottleneck theory of deep learning,” J. Stat. Mech. Theory Exp., vol. 2019, no. 12, p. 124020, 2019. doi: 10.1088/1742-5468/ab3985
[16] S. Vasudevan, “Dynamic learning rate using Mutual Information,” Arxiv preprint. pp. 1–11, 2018. Available at: Google Scholar
[17] N. O. Hodas and P. Stinis, “Doing the Impossible: Why Neural Networks Can Be Trained at All,” Front. Psychol., vol. 9, p. 1185, Jul. 2018, doi: 10.3389/fpsyg.2018.01185.
[18] R. D. Hjelm et al., “Learning deep representations by mutual information estimation and maximization,” Arxiv preprintiv preprint. 2018. Available at: Google Scholar
[19] J. Bullock, A. Luccioni, K. Hoffman Pham, C. Sin Nga Lam, and M. Luengo-Oroz, “Mapping the landscape of Artificial Intelligence applications against COVID-19,” J. Artif. Intell. Res., vol. 69, pp. 807–845, Nov. 2020, doi: 10.1613/jair.1.12162.
[20] T. Ai et al., “Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases,” Radiology, vol. 296, no. 2, pp. E32–E40, Aug. 2020, doi: 10.1148/radiol.2020200642.
[21] B. Ghoshal and A. Tucker, “Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection.” pp. 1–14, 2020. Available at: Google Scholar
[22] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput. Biol. Med., vol. 121, no. April, p. 103792, 2020, doi: 10.1016/j.compbiomed.2020.103792.
[23] G. Hub, “Open database of COVID-19 cases with chest X-ray or CT images,” 2022. [Online]. Available: github.com.
[24] Paul Mooney, “Chest X-Ray Images (Pneumonia),” Kaggle, 2018. [Online]. Available: kaggle.com.
[25] M. I. Belghazi et al., “Mutual information neural estimation,” in International conference on machine learning, 2018, pp. 531–540. Available at: Google Scholar
[26] H. M. Huan Liu, Feature Extraction, Construction and Selection. Boston, MA: Springer US, 1998. Available at: Google Books
[27] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Sci. Rep., vol. 10, no. 1, p. 19549, Dec. 2020, doi: 10.1038/s41598-020-76550-z.
[28] M. R. Karim, T. Dohmen, M. Cochez, O. Beyan, D. Rebholz-Schuhmann, and S. Decker, “DeepCOVIDExplainer: Explainable COVID-19 Diagnosis from Chest X-ray Images,” in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020, pp. 1034–1037, doi: 10.1109/BIBM49941.2020.9313304.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571 (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
andri.pranolo.id@ieee.org (publication issues)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0