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COVID-19 In Vitro Diagnostic Devices and Test Methods Database

Scientific literature on COVID-19 Test Methods and Devices - detail

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods

Detection Principle
Imaging-CT scan
Target
CT scan
Testing Method Category
In_silico
Testing Method
deep learning
Testing Method - Additional Info
Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning.
Reported Performance
sensitivity: 91.97% (2-fold cross-validation), 94.04% (10-fold cross-validation); specificity: 98.91% (2-fold cross-validation), 99.01% (10-fold cross-validation); accuracy: 94.73% (2-fold cross-validation), 95.99% (10-fold cross-validation); F-1 score: 90.58% (2-fold cross-validation), 92.84% (10-fold cross-validation); AUC values: 98.88% (2-fold cross-validation), 99.03% (10-fold cross-validation)
Sample Size
1396 images
Peer-reviewed
yes

The database contains available information from scientific literature that is being updated periodically. Please note that the provided information (as retrieved from analysed papers) is provided only for devices commercially available with CE-IVD mark. Acknowledgements