MGWO-CNN: A bio-inspired deep learning approach for COVID-19 detection in chest X-ray images - Ogundokun, Roseline Oluwaseun; Owolawi, Pius Adewale; Du, Chunling (Edelweiss Applied Science and Technology, 2025)
The present COVID-19 pandemic demands rapid and accurate diagnostic methods for early detection, isolation, and treatment. This study aims to enhance COVID-19 diagnosis with the help of bio-inspired deep learning and feature selection. This paper proposes a novel diagnostic model—MGWO-CNN—integrating a Modified Grey Wolf Optimizer (MGWO) with a Convolutional Neural Network (CNN). The framework utilizes Gabor filters as feature detectors and MGWO to optimize feature selection and improve classification performance. The model was tested and trained on a publicly available Kaggle dataset of 13,758 CXR images. Experimental results show that the MGWO-CNN outperforms traditional methods with 99.89% accuracy, 100% sensitivity, and 99.79% specificity. The model effectively distinguishes between COVID-19 and normal cases with significantly lower training and validation loss than baseline models. MGWO-CNN provides higher diagnostic accuracy, validating the effectiveness of bio-inspired optimization-based hybrid deep learning for COVID-19 diagnosis. The model offers an affordable, effective diagnostic tool that can be adopted in resource-constrained healthcare settings with limited access to high-end testing equipment.