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

COVID-19 disease has been announced as a pandemic by WHO organization. It leads to thousands of death in short period of time. Accurate and rapid diagnosis of COVID-19 suspected cases plays an essential role in its containment. An Artificial Intelligence-inspired Model for COVID-19 Diagnosis and Prediction for Patient Response to Treatment (AIMDP) is presented. Different AI techniques have been utilized in this model. CNNs is used as a deep learning technique for segmenting the CT images. WOA algorithm is used in the Feature selection phase to select the minimum set of relevant features of diagnosing COVID-19 to optimize the results obtained. An intelligent classifier selector is implemented to select, from three different classifiers, the most appropriate classifier based on the classification error obtained for each case. An automatic diagnosing phase is implemented to accurately distinguish COVID-19 from other viral pneumonias cases, using CT scan images with the most relevant signs of CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.