A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

The COVID-19 pandemic has created a major challenge for countries all over the world and placed tremendous pressure on their public health care services. An early diagnosis of may reduce impact coronavirus. To achieve this objective, modern computation methods, such as deep learning, be applied. In study, computational model involving learning biogeography-based optimization (BBO) detection management is introduced. Specifically, BBO used layer selection process in proposed convolutional neural network (CNN). accepts images, CT scans, X-rays, positron emission tomography, lung ultrasound, magnetic resonance imaging, inputs. comparative analysis, CNN compared with other existing models, namely, VGG16, InceptionV3, ResNet50, MobileNet. fitness function formation, classification accuracy considered to enhance prediction capability model. Experimental results demonstrate that outperforms InceptionV3 ResNet50.