Concurrent Convolutional Neural Networks with Decision Fusion to Diagnose COVID-19 using Chest X-ray Imagery

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...

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Bibliographic Details
Published in2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) pp. 1 - 4
Main Authors Ghenea, Gabriela-Loredana, Neagoe, Victor-Emil
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2021
Subjects
chest X-ray imagery
Computer architecture
Computers
concurrent convolutional neural networks (CCNNs)
COVID-19
COVID-19 diagnosis
decision fusion
Deep learning
Geometry
Training
Visualization
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DOI10.1109/ECAI52376.2021.9515174

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Summary: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.
DOI:10.1109/ECAI52376.2021.9515174