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

In the proposed model, different AI techniques are used based on their functionality on six main phases, as shown in figure 2 , namely, the pre-processing, segmentation, feature selection, classification, Prediction and diagnosis recommendation phase. The AIMDP model has two main modules: The Diagnosing Module (DM) which is concerned with detecting the patients with Coronavirus (COVID 19) Disease and distinguish it from other viral pneumonias, by detecting the most relevant features using feature selection and classification phases. After the patient has been detected as a COVID 19 case, the doctors must decide which patient will be provided with treatment and intensive care service. It is worth mentioned that not all cases have the same response to treatment. When there is a lack of resources, it will be mandatory to give treatment to only responding patients. For this reason, the Prediction Module(PM) is used to predict different patients' responses to COVID 19 treatment. And based on this prediction, the doctor will decide the best treatment regime for patients. Considering the lack of resources and the gradually growth of COVID19 cases, an early assessment of the patient response to treatment is essential, especially for non-responders. In addition, if . 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.