Set-Valued Medical Image Classification with Evidential CNN: A First Test with Covid-19 Dataset

Deep learning based models have been achieving ever high accuracy for precise image classification. However, in the medical sector where decisions should to be made more cautiously and where inaccuracy is less of a concern than precision and recall, it might be more appropriate to resort to imprecise classifiers, e.g. set-valued classifiers. In this work, an evidential convolutional neural network (ECNN) method is applied for set-valued medical image classification with Covid-19 X-ray dataset. Experimental result shows that the ECNN classifier is able to assign confusing image patterns to multi-class sets, while maintaining high accuracy compared to traditional probabilistic CNN classifiers. This result reveals that the ECNN classifier holds good promise of being applied for imprecise medical image classification.