Author: Ghenea, G. L.; Neagoe, V. E.
Title: Concurrent Convolutional Neural Networks with Decision Fusion to Diagnose COVID-19 using Chest X-ray Imagery Cord-id: eazrbmky Document date: 2021_1_1
ID: eazrbmky
Snippet: This paper presents a new deep learning classifier model based on the ensemble of two concurrent Convolutional Neural Networks (CNNs). The CNN modules have identical architectures according to Visual Geometry Group Network (VGG-Net) pattern, but they are intentionally trained with asymmetric volumes of training samples. The system uses a decision fusion to increase the classification accuracy. We have applied the proposed decision fusion classifier to COVID-19 diagnosis using chest X-ray imagery
Document: This paper presents a new deep learning classifier model based on the ensemble of two concurrent Convolutional Neural Networks (CNNs). The CNN modules have identical architectures according to Visual Geometry Group Network (VGG-Net) pattern, but they are intentionally trained with asymmetric volumes of training samples. The system uses a decision fusion to increase the classification accuracy. We have applied the proposed decision fusion classifier to COVID-19 diagnosis using chest X-ray imagery. For experiments, we have chosen a balanced dataset containing 5674 training chest X-ray images (2837 belonging to subjects with COVID-19, and the other 2837 corresponding to subjects non COVID-19). We have obtained a maximum accuracy of 95.43% on the test set using decision fusion. © 2021 IEEE.
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