A Neuroimaging ML-Based Framework for Anosmia Grading in Covid-19
Document Type
Conference Proceeding
Source of Publication
Proceedings - International Symposium on Biomedical Imaging
Publication Date
1-1-2024
Abstract
SARS-CoV-2 is the cause of the acute respiratory illness COVID-19. In order to identify whether a COVID-19 patient has normal, moderate, or severe anosmia, this study suggests a computer-assisted diagnostic system. It utilizes fluid-attenuated inversion recovery Magnetic Resonance Imaging and Diffusion Tensor Imaging to extract the olfactory nerve's appearance, morphology, and diffusivity markers. The suggested CAD system consists of the following stages: (1) extract appearance markers, morphology markers, and diffusivity markers, (2) apply markers fusion, and (3) select the most promising classifier as the basis for the decision and the relevant performance indicators. The current work is unique in that it uses an ensemble of the trained, tuned, and improved ML classifiers to identify OB anosmia using majority voting. From the fusion tests using different folds, stacking boosted the weight accuracy, weighted precision, weighted specificity, weighted sensitivity, and weighted F1-score to 85.30%, 80%, 89.50%, 78.90%, and 78.30% respectively. Regarding the various performance criteria, the LGBM outperforms other ML classifiers.
DOI Link
ISBN
[9798350313338]
ISSN
Publisher
IEEE
Disciplines
Computer Sciences | Medicine and Health Sciences
Keywords
Anosmia Grading, COVID-19, Features Engineering, Machine Learning
Scopus ID
Recommended Citation
Balaha, Hossam Magdy; Elgendy, Mayada; Alksas, Ahmed; Shehata, Mohamed; Alghamdi, Norah Saleh; Taher, Fatma; Ghazal, Mohammed; Ghoneim, Mahitab; Hamed, Eslam; Sherif, Fatma; Elgarayhi, Ahmed; Sallah, Mohammed; Salem, Mohamed Abdelbadie; Kamal, Elsharawy; Sandhu, Harpal; and El-Baz, Ayman, "A Neuroimaging ML-Based Framework for Anosmia Grading in Covid-19" (2024). All Works. 6815.
https://zuscholars.zu.ac.ae/works/6815
Indexed in Scopus
yes
Open Access
no