Abstract
COVID-19 (Coronavirus Disease 2019) has impacted many lives globally. Though vaccines have been found recently, many people lose their lives due to lack of early detection of the disease. Deep learning has gained significant interest in detecting COVID-19 through the use of image modalities. Conventional works have also attempted to attain better outcome in COVID-19 detection. However, they need further improvement with respect to accuracy. Applying image processing steps on data set before the application of deep learning (DL) model seems to improve the performance. The objective of proposed research work is to enhance the performance of deep learning models by using image denoising and feature extraction. Performance of proposed Local binary pattern (LBP)-DL is tested against conventional DL Models and found to be more efficient and accurate in COVID-19 detection.
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Sagar, P.Y., Dhanalakshmy, D.M. (2023). Empirical Evaluation of Deep Learning Models with Local Binary Pattern for COVID-19 Detection. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_39
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DOI: https://doi.org/10.1007/978-981-19-5292-0_39
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