Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images

Sally M. ELGhamrawy; Abou Ellah Hassanien

These selected features are passed to the classification phase, that use some additional data from RT-PCR and Complete Blood Count (CBC), when needed, to accurately classify the patients based on their viral pneumonias signs and features. AIMDP model uses the classifier selector module to choose the classifier with most accurate value for the tested case. The tested classifiers are: SVM, Naive Bayes (NB), and Discriminant Analysis (DA) to test the performance with different perspectives. In the proposed model, the FSWOA phase provides the classifier with number of whales, each whale signifies a group of features "1" means that the feature is selected and "0" means not selected. Thus, WOA search to find the most relevant set of features that achieves the highest accuracy with either SVM, NB or DA classifier and the fitness function is utilized. The outcome of the classification is estimated based on the values of optimum features obtained from CT scan. And if the case is suspected (not confirmed) as a COVID-19 case, then further lab evaluations must be considered for accurate classification. The classification phase main goal is to differentiate COVID-19 patient from other infections. After classifying the data, the model is trained and validated in this layer. A confusion matrix is produced as a graphic form of the performance. each row refers to the instances in its real class while each column refers to the instances in a predicted class. Based on this matrix, the sensitivity, specificity, accuracy and f-measure are calculated to evaluate the classifier.