Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images
| Autor: | Chiagoziem C. Ukwuoma, Dongsheng Cai, Md Belal Bin Heyat, Olusola Bamisile, Humphrey Adun, Zaid Al-Huda, Mugahed A. Al-antari |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of King Saud University: Computer and Information Sciences, Vol 35, Iss 7, Pp 101596- (2023) |
| Druh dokumentu: | article |
| ISSN: | 1319-1578 43039561 |
| DOI: | 10.1016/j.jksuci.2023.101596 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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