Home⇛PUP Journal of Science and Technology⇛vol. 14 no. 1 (2021)

INVESTIGATION ON THE EFFECTS OF RADIOGRAPHIC IMAGE QUALITY ATTRIBUTES ON THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) IN DETECTING COVID-19

Reymond R Mesuga | Cloyd Raymond P Pernes | Luther A Villacruz | Mark Anthony C Burgonio

Discipline: bioengineering, medical and biomedical engineering

 

Abstract:

Radiographic image quality is one of the factors that impacts professionals’ decisions when diagnosing lung diseases using X-ray images. Hence, poor radiographic image quality could result in a misleading diagnosis affecting the person being investigated. This is true in human vision, as well as the computer vision. This study investigated the effects of different radiographic image quality attributes (i.e., contrast, Gaussian blur, Gaussian noise, and salt-and-pepper noise) on the performance of various Convolutional Neural Networks (CNNs) models. We use COVID-19 x-ray data as an initiative to the pandemic, apply different radiographic image quality attributes, and test the performance of CNN models in the effects of the attributes in the classification task. The results showed the following: (i) increasing levels of experimented noises (i.e., Gaussian and salt-and-pepper noise) rapidly decreases the performance of the models with no sign of resiliency; (ii) decreasing contrast appears to be beneficial at some particular level (e.g., contrast factor = 3); and (iii) increasing Gaussian blur decreases the performance of models but less rapidly than that of noises. As a conclusion, increasing noise like Gaussian and salt-and-pepper noise can be considered as a hindrance to the performance of CNNs while decreasing contrast and increasing Gaussian blur seemed to be beneficial especially if applied for data augmentation or enhancement techniques as the performance of the CNNs were observed to be more resilient against these two attributes than that of noises.



References:

  1. Alqudah, A. M. (2020). Augmented COVID-19 x-ray images dataset.
  2. Basu, S., Mitra, S., & Saha, N. (2020). Deep learning for screening covid-19 using chest x-ray images. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2521-2527). IEEE.
  3. Boyat, A. K., & Joshi, B. K. (2015). A review paper: Noise models in digital image processing. arXiv preprint arXiv:1505.03489.
  4. Bradski, G., & Kaehler, A. (2000). OpenCV. Dr. Dobb’s Journal of Software Tools, 3(2). Carpentries, T. (2021). Blurring images. https://datacarpentry.org/image-processing/06blurring/
  5. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
  6. Clark, A. (2021). Pillow 8.2.0. https://pypi.org/project/split-folders/#description
  7. Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). Covid 19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988.
  8. Dodge, S., & Karam, L. (2016). Understanding how image quality affects deep neural networks. In 2016 Eighth international conference on quality of multimedia experience (QoMEX) (pp. 1-6). IEEE.
  9. El-Shafai, W., & Abd El-Samie, F. (2020). Extensive COVID-19 x-ray and CT chest images dataset. Mendeley Data, 3(10).
  10. Guissous, A. E. (2019). Skin lesion classification using deep neural network. arXiv preprint arXiv:1911.07817.
  11. Haeberli, P., & Voorhies, D. (1994). Image processing by linear interpolation and extrapolation. IRIS Universe Magazine, 28, 8-9.
  12. Hammoudi, K., Benhabiles, H., Melkemi, M., Dornaika, F., Arganda-Carreras, I., Collard, D., & Scherpereel, A. (2021). Deep learning on chest x-ray images to detect and evaluate pneumonia cases at the era of COVID-19. Journal of Medical Systems, 45(7), 75.
  13. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
  14. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
  15. Jain, R., Gupta, M., Taneja, S., & Hemanth, D. J. (2021). Deep learning based detection and analysis of COVID-19 on chest x-ray images. Applied Intelligence, 51, 16901700.
  16. Ker, J., Wang, L., Rao, J., & Lim, T. (2017). Deep learning applications in medical image analysis. IEEE Access, 6, 9375-9389.
  17. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., & Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 11221131.
  18. Kushol, R., Raihan, M., Salekin, M. S., & Rahman, A. B. M. (2019). Contrast enhancement of medical x-ray image using morphological operators with optimal structuring element. arXiv preprint arXiv:1905.08545.
  19. Lee, W., Lee, S., Chong, S., Lee, K., Lee, J., Choi, J. C., & Lim, C. (2020). Radiation dose reduction and improvement of image quality in digital chest radiography by new spatial noise reduction algorithm. Plos One, 15(2), e0228609.
  20. López-Cabrera, J. D., Orozco-Morales, R., Portal-Diaz, J. A., Lovelle-Enríquez, O., & Pérez-Díaz, M. (2021). Current limitations to identify COVID-19 using artificial intelligence with chest x-ray imaging. Health and Technology, 11(2), 411-424.
  21. Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sensing, 10(7), 1119.
  22. Rajpal, G. (2020). A comprehensive guide to convolution neural network. https://medium.com/swlh/a-comprehensive-guide-to-convolution-neural-network86f931e55679
  23. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115, 211.
  24. Sachan, A. (2017). Detailed guide to understand and implement ResNets. URL: https://cvtricks.com/keras/understand-implement-resnets/.
  25. Simonyan, K. & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  26. Swain, A. (2018). Noise in digital image processing. https://medium.com/imagevision/noise-in-digital-image-processing-55357c9fab7
  27. Szegedy, C., Vanhoucke, V., Loffe, S., Shlens, J., &  Wojna, Z. (2016). Rethinking the Inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  28. Wang, L., Wong, A., Lin, Z. Q., McInnis, P., Chung, A., Gunraj, H., Lee, J., Ross, M., VanBerlo, B., Ebadi, A., & Al-Haimi, A. (2020). Actualmed COVID-19 chest x-ray dataset initiative. chestxraydataset URL: https://github.com/agchung/Actualmed-COVID
  29. Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2097-2106).

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