DOI: 10.7763/IJCTE.2026.V18.1390
Combining Bioinspired Red Kite Optimization and Deep Learning for Effective COVID-19 Detection in Chest Radiography
2. School of Computer Science & Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, India
3. Department of Artificial Intelligence and Data Science, Faculty of Science and Technology (ICFAITECH), ICFAI Foundation for Higher Education, Hyderabad, India
4. School of Computer Applications, KIIT Deemed to be Univsity, Bhuabneswar, Odisha, India
5. Department of Electrical Engineering, College of Engineering Wadi Addawasir, Prince Sattam bin Abdulaziz University, Saudi Arabia
Email: m.altaf@psau.edu.sa (M.A.A.); s.alnatheer@psau.edu.sa (S.A.); sachinandan09@gmail.com (S.N.M.); pavanpk@ifheindia.org (P.P.K.); bibhuti.dash@gmail.com (B.B.D.); q.mohammed@psau.edu.sa (Q.M.)
*Corresponding author
Manuscript received June 14, 2025; revised July 24, 2025; accepted November 25, 2025; published March 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
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.
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).