Comparative study for automated coronavirus detection in CT images with transfer learning

Abstract EN

Purpose : to design a computer-aided diagnosis system with transfer learning methods to serve as decision support system for automated coronavirus detection in CT images.

methods : four pre-trained deep convolutional neural networks (resnet-18, squeezenet, shufflenet, mobile net-v2) have been investigated to diagnose coronavirus with CT scans.

to evaluate the pre-trained deep convolutional neural network, we used the Covid-CT dataset, which contains 349 CT images of covid-19 from 216 patients, and 397 CT images obtained from non covid-19 subjects.

results : considering binary classification performance results, it has been seen that the pre trained resnet-18 model provides the highest classification performance (97.0470 +5.5466 accuracy, 98.7342 ± 2.1925 sensitivity, 95.1429 ± 9.3460 specificity, and 0.9737± 0.0489 f1-score) among other three used models.

conclusion : resnet-18 model can be employed as a supportive decision-making system to assist radiologists at clinics and hospitals to screen patients swiftly.