A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis

Lobna M AbouEl-Magd; Ashraf Darwish; Vaclav Snasel; Aboul Ella Hassanien

doi:10.1007/s10586-022-03703-2

A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis

Cluster Comput. 2023;26(2):1389-1403. doi: 10.1007/s10586-022-03703-2. Epub 2022 Aug 23.

Authors

Lobna M AbouEl-Magd^{1

2}, Ashraf Darwish^{3

2}, Vaclav Snasel⁴, Aboul Ella Hassanien^{5

2}

Affiliations

¹ Computer Science Department, Misr Higher Institute, Mansoura, Egypt.
² Scientific Research Group in Egypt (www.egyptscience.net), Giza, Egypt.
³ Faculty of Science, Helwan University, Helwan, Egypt.
⁴ VSB-Technical University of Ostrava, Ostrava, Czech Republic.
⁵ Faculty of Computers and AI, Cairo University, Giza, Egypt.

Abstract

Coronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to its ability to define features such as perspective, orientation, and size. Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure that new samples were generated close to the sample center, avoiding the production of outliers or changes in data distribution. As the results may change by changing capsule network parameters (Capsule dimensionality and routing number), the Gaussian optimization method has been used to optimize these parameters. Four experiments have been done, (1) CapsNet with the unbalanced data sets, (2) CapsNet with balanced data sets based on class weight, (3) CapsNet with balanced data sets based on SMOTE, and (4) CapsNet hyperparameters optimization with balanced data sets based on SMOTE. The performance has improved and achieved an accuracy rate of 96.58% and an F1- score of 97.08%, a competitive optimized model compared to other related models.

Keywords: COVID-19; Capsule Neural Networks; Convolution neural networks; Coronavirus; Gaussian optimization method; VGG16.