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