Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.

ABSTRACT:

Background

Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data.

Methods

We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers

SUBMITTER: Tehrani SSM 

PROVIDER: S-EPMC10656999 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature