doi: 10.7763/IJCTE.2026.V18.1390
Combining Bioinspired Red Kite Optimization and Deep Learning for Effective COVID-19 Detection in Chest Radiography
Mohammed Altaf Ahmed1,*
,
Suleman Alnatheer1 ,
Sachi Nandan Mohanty2 ,
Paruchuri Pavan Kumar3 ,
Bibhuti Bhushan Dash4 ,
Qutubuddin Mohammed5
- 1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- 2School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
- 3Department of Artificial Intelligence and Data Science, Faculty of Science and Technology (ICFAITECH), ICFAI Foundation for Higher Education, Hyderabad, India
- 4School of Computer Applications, KIIT Deemed to be Univsity, Bhuabneswar, Odisha, India
- 5Department of Electrical Engineering, College of Engineering Wadi Addawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia
- Manuscript receivedJune 14, 2025
- revisedJuly 24, 2025
- acceptedNovember 25, 2025
- publishedMarch 31, 2026
Abstract
Deep Learning (DL)-based systems, employing advanced growths, pave the way for bioinspired methods in almost all domains of life. Healthcare organizations can employ DL approaches due to their precision in recognizing and identifying distinct diseases. The coronavirus disease (COVID-19) epidemic has emerged as the most dangerous disease in recent times, posing a significant burden on health organizations worldwide. Medical imaging and PCR testing have the potential to analyze COVID-19. Given the high spreadability of COVID-19, Chest X-Ray (CXR) analysis is considered safe under various conditions. DL systems are capable of enhancing medical imaging tools and supporting radiologists in making medical decisions for the analysis, diagnosis, and monitoring of distinct diseases. With this motivation, this study presents a novel bioinspired red kite optimizer with a DL-based COVID-19 classification (BRKODL-COVIDC) method on CXR images. The BRKODL-COVIDC method intends to recognize and categorize the presence of COVID-19 using DL models. In the presented BRKODL-COVIDC method, the Bilateral Filtering (BF) model can be used for image pre-processing tasks. In addition, the complex patterns and features in the images can be derived from the DenseNet121 model. For optimal hyperparameter selection of the DL techniques, the Random Key Optimizer (RKO) model can be employed in this research. Last, the BRKODL-COVIDC technique makes use of the Dilated Convolutional Auto-Encoder (DCAE) model and is used for identification purposes. Thus, with this research study, we are contributing to the National Priorities for Research, Development, and Innovation (RDI) in health and wellness to provide and maintain a sustainable environment in the health sector. The simulation outputs of the BRKODL-COVIDC technique can be examined on a standard dataset. The simulated outputs demonstrated the enhanced performance of the BRKODL-COVIDC technique in the COVID-19 detection procedure.
Keywords
- computer-aided diagnosis
- bioinspired algorithms
- deep learning
- intelligent systems
- leukaemia cancer
- sustainable environment
- healthcare
How to Cite
Mohammed Altaf Ahmed, Suleman Alnatheer, Sachi Nandan Mohanty, Paruchuri Pavan Kumar, Bibhuti Bhushan Dash, and Qutubuddin Mohammed, "Combining Bioinspired Red Kite Optimization and Deep Learning for Effective COVID-19 Detection in Chest Radiography," International Journal of Computer Theory and Engineering, vol. 18, no. 1, pp. 79-87, 2026. https://doi.org/10.7763/IJCTE.2026.V18.1390
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).