Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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.

An Improved DeepNN with Feature Ranking for Covid-19 Detection

The outbreak of Covid-19 has taken the lives of many patients so far.The symptoms of COVID-19 include muscle pains,loss of taste and smell,coughs,fever,and sore throat,which can lead to severe cases of breathing difficulties,organ failure,and death.Thus,the early detection of the virus is very crucial.COVID-19 can be detected using clinical tests,making us need to know the most important symptoms/features that can enhance the decision process.In this work,we propose a modified multilayer perceptron(MLP)with feature selection(MLPFS)to predict the positive COVID-19 cases based on symptoms and features from patients’electronic medical records(EMR).MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance.Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy.Experiments were conducted using three different COVID-19 datasets and eight different models,including the proposed MLPFS.Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models.Additionally,it outperforms the other models in classification results as well as time.