Abstract
Medical imaging has been intensively used to help the radiologists do the correct diagnosis for the COVID-19 disease. In particular, chest X-ray imaging is one of the prevalent information sources for COVID-19 diagnosis. The obtained images can be viewed as numerical data and processed by non-negative matrix factorization (NMF) algorithms, one of the available numerical data analysis tools.
In this work, we propose a new sparse semi-NMF algorithm that can classify the patients into COVID-19 and normal patients, based on chest X-ray images. We show that the huge volume of data resulting from X-ray images can be significantly reduced without significant loss of classification accuracy. Then, we evaluate our algorithm by carrying out an experiment on a publicly available dataset, having a known chest X-ray image bi-partition.
Experimental results demonstrate that the proposed sparse semi-NMF algorithm can predict COVID-19 patients with high accuracy,compared to state-of-the-art algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The proof the this theorem is omitted in this short version of the paper.
References
Cichocki, A., Phan, A.H.: Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 92(3), 708–721 (2009)
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988 (2020)
Ding, C., He, X., Simon, H.D.: On the equivalence of nonnegative matrix factorization and spectral clustering. In: SIAM International Conference on Data Mining, pp. 606–610 (2005)
Gillis, N.: The why and how of nonnegative matrix factorization. CoRR abs/1401.5226http://arxiv.org/abs/1401.5226 (2014)
Hartigan, J., Wong, M.: Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)
Kim, H., Park, H.: Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23(12), 1495–1502 (2007)
Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)
Laurberg, H., Christensen, M.G., Plumbley, M.D., Hansen, L.K., Jensen, S.H.: Theorems on positive data: On the uniqueness of NMF. Comput. Intell. Neurosci. 2008, 764206 (2008)
Naanaa, W., Nuzillard, J.: Extreme direction analysis for blind separation of nonnegative signals. Signal Process. 130, 254–267 (2017)
Pentti, P.: Tapper unto: positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994)
Ucara, F., Korkmaz, D.: COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med. Hypotheses 140, 109761 (2020)
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.: CovidGAN: data augmentation using auxiliary classifier GAN for improved Covid-19 detection. IEEE Access 99, 1–1 (2020)
Wang, Z., He, M., Wang, L., Xu, K., Xiao, J., Nian, Y.: Semi-NMF-based reconstruction for hyperspectral compressed sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4352–4368 (2020). https://doi.org/10.1109/JSTARS.2020.3010332
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sekma, M., Mhamdi, A., Naanaa, W. (2022). Approximating Sparse Semi-nonnegative Matrix Factorization for X-Ray Covid-19 Image Classification. In: Bădică, C., Treur, J., Benslimane, D., Hnatkowska, B., Krótkiewicz, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2022. Communications in Computer and Information Science, vol 1653. Springer, Cham. https://doi.org/10.1007/978-3-031-16210-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-16210-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16209-1
Online ISBN: 978-3-031-16210-7
eBook Packages: Computer ScienceComputer Science (R0)