Covid-19 classification with deep neural network and belief functions
This addresses the need for faster and more reliable Covid-19 diagnosis for medical professionals, but it appears incremental as it builds on existing deep learning methods with belief functions for improved explainability.
The paper tackled the problem of automatic Covid-19 detection from CT images, which is time-consuming for radiologists, by proposing a belief function-based convolutional neural network with semi-supervised training, achieving an accuracy of 0.81, F1 of 0.812, and AUC of 0.875.
Computed tomography (CT) image provides useful information for radiologists to diagnose Covid-19. However, visual analysis of CT scans is time-consuming. Thus, it is necessary to develop algorithms for automatic Covid-19 detection from CT images. In this paper, we propose a belief function-based convolutional neural network with semi-supervised training to detect Covid-19 cases. Our method first extracts deep features, maps them into belief degree maps and makes the final classification decision. Our results are more reliable and explainable than those of traditional deep learning-based classification models. Experimental results show that our approach is able to achieve a good performance with an accuracy of 0.81, an F1 of 0.812 and an AUC of 0.875.