Diagnosis of Covid-19 using Fractional B-spline Wavelet Transform in Lung Ultrasound images

Document Type : Article


Department of Biomedical Engineering, College of Medical Science and Technologies, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran


The coronavirus spread rapidly in the world and caused the disease of Covid-19. The proposed research was conducted with the aim of classifying people with Covid-19 from other people. Among all the imaging modalities, lung ultrasound images were used to diagnose covid-19.
The open source Point-Care-of-Ultrasound (POCUS) database was collected, which contained 59 Lung Ultrasound (LUS) images. In this research, KNN classifier with K-fold cross validation was used to classify the feature matrix obtained from Fractional B-spline Wavelet Transform (FBSWT). In the proposed method, Block-Matching and 3D filtering (BM3D) filter was used in some methods, which had acceptable results.
The proposed method was used to classify healthy people from patients with Covid-19. The results show that when the features based on the wavelet transform (WT) are used, the proposed method can be achieved 90.90% sensitivity, 92.30% specificity, and 91.42% accuracy. While the features extracted from FBSWT show that the proposed method can be achieved 95.45% sensitivity, 93.30% specificity, and 94.28% accuracy.
Fractional transforms, especially the FBSWT, can be a useful tool for image processing. They can be used for various purposes such as detection and classification. By using FBSWT, it is possible to accurately diagnose the disease of covid-19.


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