COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models

Sara El-Ateif, Ali Idri

2022

Abstract

COVID-19 is a recently emerged pneumonia disease with threatening complications that can be avoided by early diagnosis. Deep learning (DL) multimodality fusion is rapidly becoming state of the art, leading to enhanced performance in various medical applications such as cognitive impairment diseases and lung cancer. In this paper, for COVID-19 detection, seven deep learning models (VGG19, DenseNet121, InceptionV3, InceptionResNetV2, Xception, ResNet50V2, and MobileNetV2) using single-modality and joint fusion were empirically examined and contrasted in terms of accuracy, area under the curve, sensitivity, specificity, precision, and F1-score with Scott-Knott Effect Size Difference statistical test and Borda Count voting method. The empirical evaluations were conducted over two datasets: COVID-19 Radiography Database and COVID-CT using 5-fold cross validation. Results showed that MobileNetV2 was the best performing and less sensitive technique on the two datasets using mono-modality with an accuracy value of 78% for Computed Tomography (CT) and 92% for Chest X-Ray (CXR) modalities. Joint fusion outperformed mono-modality DL techniques, with MobileNetV2, ResNet50V2 and InceptionResNetV2 joint fusion as the best performing for COVID-19 diagnosis with an accuracy of 99%. Therefore, we recommend the use of the joint fusion DL models MobileNetV2, ResNet50V2 and InceptionResNetV2 for the detection of COVID-19. As for mono-modality, MobileNetV2 was the best in performance and less sensitive model to the two imaging modalities.

Paper Citation

in Harvard Style

El-Ateif S. and Idri A. (2022). COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 160-167. DOI: 10.5220/0010897100003123

in Bibtex Style

@conference{bioimaging22,
author={Sara El-Ateif and Ali Idri},
title={COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={160-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010897100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models
SN - 978-989-758-552-4
AU - El-Ateif S.
AU - Idri A.
PY - 2022
SP - 160
EP - 167
DO - 10.5220/0010897100003123
PB - SciTePress