Metaheuristic Optimization Through Deep Learning Classification of燙OVID-19 in Chest X-Ray Images
Nagwan Abdel Samee, El‐Sayed M. El‐kenawy, Ghada Atteia, Mona Jamjoom, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Noha E. El-Attar, Tarek Gaber, Adam Słowik, Mahmoud Y. Shams
Abstract
As corona virus disease (COVID-19) is still an ongoing global outbreak, countries around the world continue to take precautions and measures to control the spread of the pandemic. Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals, a rapid, reliable, and automatic detection of COVID-19 is in extreme need to curb the number of infections. By analyzing the COVID-19 chest X-ray images, a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers. The lung region was segmented from the original chest X-ray images and augmented using various transformation operations. Furthermore, the augmented images were fed into the VGG19 deep network for feature extraction. On the other hand, a feature selection method is proposed to select the most significant features that can boost the classification results. Finally, the selected features were input into an optimized neural network for detection. The neural network is optimized using the proposed hybrid optimizer. The experimental results showed that the proposed method achieved 99.88% accuracy, outperforming the existing COVID-19 detection models. In addition, a deep statistical analysis is performed to study the performance and stability of the proposed optimizer. The results confirm the effectiveness and superiority of the proposed approach.