COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network

Andronicus A Akinyelu; Bubacarr Bah

doi:10.3390/diagnostics13081484

COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network

Diagnostics (Basel). 2023 Apr 20;13(8):1484. doi: 10.3390/diagnostics13081484.

Authors

Andronicus A Akinyelu^{1

2}, Bubacarr Bah^{1

3}

Affiliations

¹ Research Centre, African Institute for Mathematical Sciences (AIMS) South Africa, Cape Town 7945, South Africa.
² Department of Computer Science and Informatics, University of the Free State, Phuthaditjhaba 9866, South Africa.
³ Department of Mathematical Sciences, Stellenbosch University, Cape Town 7945, South Africa.

Abstract

This study proposes a deep-learning-based solution (named CapsNetCovid) for COVID-19 diagnosis using a capsule neural network (CapsNet). CapsNets are robust for image rotations and affine transformations, which is advantageous when processing medical imaging datasets. This study presents a performance analysis of CapsNets on standard images and their augmented variants for binary and multi-class classification. CapsNetCovid was trained and evaluated on two COVID-19 datasets of CT images and X-ray images. It was also evaluated on eight augmented datasets. The results show that the proposed model achieved classification accuracy, precision, sensitivity, and F1-score of 99.929%, 99.887%, 100%, and 99.319%, respectively, for the CT images. It also achieved a classification accuracy, precision, sensitivity, and F1-score of 94.721%, 93.864%, 92.947%, and 93.386%, respectively, for the X-ray images. This study presents a comparative analysis between CapsNetCovid, CNN, DenseNet121, and ResNet50 in terms of their ability to correctly identify randomly transformed and rotated CT and X-ray images without the use of data augmentation techniques. The analysis shows that CapsNetCovid outperforms CNN, DenseNet121, and ResNet50 when trained and evaluated on CT and X-ray images without data augmentation. We hope that this research will aid in improving decision making and diagnostic accuracy of medical professionals when diagnosing COVID-19.

Keywords: COVID-19 diagnosis; CT scans; capsule neural network; machine learning; medical imaging.

Grants and funding

Authors were supported by a grant (no. 01DG16005) of the BMBF (through the Alexander von Humboldt Foundation), funding for the German Research Chair of Mathematics with Specialization in Data Science at AIMS South Africa.