Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images

• Published in: Journal of King Saud University. Computer and information sciences Vol. 35; no. 7; p. 101596
• Main Authors: Ukwuoma, Chiagoziem C., Cai, Dongsheng, Heyat, Md Belal Bin, Bamisile, Olusola, Adun, Humphrey, Al-Huda, Zaid, Al-antari, Mugahed A.
• Format: Journal Article
• Language: English
• Published: Saudi Arabia Elsevier B.V 01.07.2023; The Author(s). Published by Elsevier B.V. on behalf of King Saud University; Elsevier
• Subjects: COVID-19 prediction; Explainable Artificial Intelligence (XAI); Hybrid ensemble deep feature extraction; Multi-head self-attention network; Visual explainable saliency maps; Visual explainable saliency maps; Explainable Artificial Intelligence (XAI); Multi-head self-attention network; COVID-19 prediction; Hybrid ensemble deep feature extraction
• ISSN: 1319-1578; 2213-1248; 2213-1248
• DOI: 10.1016/j.jksuci.2023.101596

[Display omitted] COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.