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Found 5 papers, 5 papers with code
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images
This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images.
COVID-19 detection using ViT transformer-based approach from Computed Tomography Images
This method involves evaluating all CT slices for a given patient and assigning the patient the diagnosis that relates to the thresholding for the CT scan.
COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline
In the classification part, the results were compared at slice-level and at patient-level as well.
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images
Finally, the adaptability of the modified Xception trasnfer learning-based model to the unique features of the COV19-CT-DB dataset showcases its potential as a robust tool for enhanced COVID-19 diagnosis from CT images.
Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning.
