Research Article
Local Binary Pattern and RVFL for Covid-19 Diagnosis
@INPROCEEDINGS{10.1007/978-3-031-50571-3_23, author={Mengke Wang}, title={Local Binary Pattern and RVFL for Covid-19 Diagnosis}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2024}, month={2}, keywords={local binary pattern random vector functional link network deep residual network Covid-19}, doi={10.1007/978-3-031-50571-3_23} }- Mengke Wang
Year: 2024
Local Binary Pattern and RVFL for Covid-19 Diagnosis
ICMTEL
Springer
DOI: 10.1007/978-3-031-50571-3_23
Abstract
Recently, the use of artificial intelligence to improve the efficiency of Covid-19 diagnosis has become a trend due to the spread and proliferation of Covid-19 and the fact that healthcare professionals alone are no longer sufficient to cope with the rapid spread of Covid-19. Chest computed tomography (CT) is an effective method to diagnose Covid-19. Using image processing methods to help diagnose such images has become critical. In this trend, we propose a way to detect Covid-19 efficiently. The scheme employs a hybrid model. Local binary patterns (LBP) implement feature extraction in the preprocessing stage. Validation classification results are obtained using the random vector functional link (RVFL) network, which is finally validated by 10-fold cross-validation. It experimentally demonstrated the usefulness of our proposed model for diagnostic-level progress. It helps healthcare workers accurately identify Covid-19.
