Abstract
Diagnosis and understanding of disease progression require an interpretation of medical images, would take a lot of time in manual interpretation of the large amount of medical images accumulated. Thus, the automatic analysis and understanding of the medical images becomes an active research topic. In this case, feature extraction from the medical images plays an important role in obtaining diagnostic performance . In this context, we propose a Covid-19 cases identification based on sparse coding, wavelet analysis for feature extraction and AE for feature modeling. Our approach is based on sparse coding and wavelet analysis techniques for image representation and it is tested with the COVID-19 dataset. The experimental results demonstrate the performance of our system.
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Acknowledgment
The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Lazrag, H., Ali, R.B., Ejbali, R. (2021). Sparse Wavelet Auto-encoder for Covid-19 Cases Identification. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_3
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DOI: https://doi.org/10.1007/978-3-030-73050-5_3
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