Synergizing Nature Inspired Optimization with Deep Learning for COVID-19 Image Recognition

The worldwide pandemic disease due to corona virus also termed as COVID-19 has propelled the need for efficient and accurate diagnostic tools. The proposed work explores the integration of deep learningDeep learning methodologies with nature-inspired optimization techniques to improve the precision of COVID-19 image detection. Leveraging the power of Convolutional Neural Networks (CNNsConvolutional Neural Networks (CNNs)), our proposed framework aims to discern patterns indicative of COVID-19 infectionCOVID-19 infection from clinical imagesClinical images which includes CT scansCT scans and chest X ray (CXR imagesChest X ray (CXR images)) The incorporation of nature-inspired optimizationNature-inspired optimization methods, including bat algorithmBat algorithm and particle swarm optimizationParticle swarm optimization addresses the challenge of optimizing deep neural network architectures and parameters. By mimicking natural processes in these optimization algorithms, convergence speed and overall performance of the deep learning model is considerably improved, thereby enhancing its ability to accurately identify COVID-19-related patterns in medical imagesMedical images... Comparative analyses against traditional deep learning methods underscore the potential improvements in accuracy achieved by integrating nature-inspired optimization techniques which includes Bat algorithm and PSO algorithm.