Title
Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images
Document Type
Article
Publication Date
2-2022
Abstract
Introduction: In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. Objectives: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. Methods: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. Results: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. Conclusions: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.jksus.2022.101898
Repository Citation
Hammad, Mohamed; Tawalbeh, Lo'ai A.; Iliyasu, Abdullah M.; Sedik, Ahmed; Abd El-Samie, Fathi E.; Alkinani, Monagi H.; and Abd El-Latif, Ahmed A., "Efficient Multimodal Deep-Learning-Based COVID-19 Diagnostic System for Noisy and Corrupted Images" (2022). Computer Science Faculty Publications. 28.
https://digitalcommons.tamusa.edu/computer_faculty/28
Comments
This article was originally published as:
Mohamed Hammad, Lo'ai Tawalbeh, Abdullah M. Iliyasu, Ahmed Sedik, Fathi E. Abd El-Samie, Monagi H. Alkinani, Ahmed A. Abd El-Latif, Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images, Journal of King Saud University - Science, Volume 34, Issue 3, 2022, 101898, https://doi.org/10.1016/j.jksus.2022.101898
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)